Letter to a friend

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User Authentication Through Mouse Dynamics

16 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 User Authentication Through Mouse Dynamics Chao Shen, Student Member, IEEE, Zhongmin Cai, Member, IEEE, Xiaohong Guan, Fellow, IEEE, Youtian Du, Member, IEEE, and Roy A. Maxion, Fellow, IEEE Abstract—Behavior-based user authentication with pointing devices, such as mice or touchpads, has been gaining attention. As an emerging behavioral biometric, mouse dynamics aims to address the authentication problem by verifying computer users on the basis of their mouse operating styles.This paper presents a simple and ef? cient user authentication approach based on a ? xed mouse-operation task. For each sample of the mouse-operation task, both traditional holistic features and newly de? ned procedural features are extracted for accurate and ? ne-grained characterization of a user’s unique mouse behavior. Distance-measurement and eigenspace-transformation techniques are applied to obtain featur e components for ef? ciently representing the original mouse feature space.Then a one-class learning algorithm is employed in the distance-based feature eigenspace for the authentication task. The approach is evaluated on a dataset of 5550 mouse-operation samples from 37 subjects. Extensive experimental results are included to demonstrate the ef? cacy of the proposed approach, which achieves a false-acceptance rate of 8. 74%, and a false-rejection rate of 7. 69% with a corresponding authentication time of 11. 8 seconds. Two additional experiments are provided to compare the current approach with other approaches in the literature.Our dataset is publicly available to facilitate future research. Index Terms—Biometric, mouse dynamics, authentication, eigenspace transformation, one-class learning. I. INTRODUCTION T HE quest for a reliable and convenient security mechanism to authenticate a computer user has existed since the inadequacy of conventional password mechanism was reali zed, ? rst by the security community, and then gradually by the Manuscript received March 28, 2012; revised July 16, 2012; accepted September 06, 2012. Date of publication October 09, 2012; date of current version December 26, 2012.This work was supported in part by the NSFC (61175039, 61103240, 60921003, 60905018), in part by the National Science Fund for Distinguished Young Scholars (60825202), in part by 863 High Tech Development Plan (2007AA01Z464), in part by the Research Fund for Doctoral Program of Higher Education of China (20090201120032), and in part by Fundamental Research Funds for Central Universities (2012jdhz08). The work of R. A. Maxion was supported by the National Science Foundation under Grant CNS-0716677. Any opinions, ? dings, conclusions, or recommendations expressed in this material are those of the authors, and do not necessarily re? ect the views of the National Science Foundation. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sviatoslav Voloshynovskiy. C. Shen, Z. Cai, X. Guan, and Y. Du are with the MOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China (e-mail: [email  protected] xjtu. edu. cn; [email  protected] xjtu. edn. cn; [email  protected] xjtu. edu. cn; [email  protected] jtu. edu. cn). R. A. Maxion is with the Dependable Systems Laboratory, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: [email  protected] cmu. edu). Color versions of one or more of the ? gures in this paper are available online at http://ieeexplore. ieee. org. Digital Object Identi? er 10. 1109/TIFS. 2012. 2223677 public [31]. As data are moved from traditional localized computing environments to the new Cloud Computing paradigm (e. g. , Box. net and Dropbox), the need for better authentication has become more pressing.Recently, several large-scale password leakages exposed users to an unprecedented risk of disclosure and abuse of their information [47], [48]. These incidents seriously shook public con? dence in the security of the current information infrastructure; the inadequacy of password-based authentication mechanisms is becoming a major concern for the entire information society. Of various potential solutions to this problem, a particularly promising technique is mouse dynamics. Mouse dynamics measures and assesses a user’s mouse-behavior characteristics for use as a biometric.Compared with other biometrics such as face, ? ngerprint and voice [20], mouse dynamics is less intrusive, and requires no specialized hardware to capture biometric information. Hence it is suitable for the current Internet environment. When a user tries to log into a computer system, mouse dynamics only requires her to provide the login name and to perform a certain sequence of mouse operations. Extracted behavioral features, based on mouse movements and clicks, are compared to a legitimate user’s pro? le. A match authenticates the user; otherwise her access is denied.Furthermore, a user’s mouse-behavior characteristics can be continually analyzed during her subsequent usage of a computer system for identity monitoring or intrusion detection. Yampolskiy et al. provide a review of the ? eld [45]. Mouse dynamics has attracted more and more research interest over the last decade [2]–[4], [8], [14]–[17], [19], [21], [22], [33], [34], [39]–[41], [45], [46]. Although previous research has shown promising results, mouse dynamics is still a newly emerging technique, and has not reached an acceptable level of performance (e. . , European standard for commercial biometric technology, which requires 0. 001% false-acceptance rate and 1% false-rejection rate [10]). Most existing approaches for mouse-dynamics-based user authentication result in a low authentication accuracy or an unreasonably long authenticatio n time. Either of these may limit applicability in real-world systems, because few users are willing to use an unreliable authentication mechanism, or to wait for several minutes to log into a system.Moreover, previous studies have favored using data from real-world environments over experimentally controlled environments, but this realism may cause unintended side-effects by introducing confounding factors (e. g. , effects due to different mouse devices) that may affect experimental results. Such confounds can make it dif? cult to attribute experimental outcomes solely to user behavior, and not to other factors along the long path of mouse behavior, from hand to computing environment [21], [41]. 1556-6013/$31. 00  © 2012 IEEE SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 17It should be also noted that most mouse-dynamics research used data from both the impostors and the legitimate user to train the classi? cation or detection model. However, in the scenario of mouse-d ynamics-based user authentication, usually only the data from the legitimate user are readily available, since the user would choose her speci? c sequence of mouse operations and would not share it with others. In addition, no datasets are published in previous research, which makes it dif? cult for third-party veri? cation of previous work and precludes objective comparisons between different approaches.A. Overview of Approach Faced with the above challenges, our study aims to develop a mouse-dynamics-based user authentication approach, which can perform user authentication in a short period of time while maintaining high accuracy. By using a controlled experimental environment, we have isolated inherent behavioral characteristics as the primary factors for mouse-behavior analysis. The overview of the proposed approach is shown in Fig. 1. It consists of three major modules: (1) mouse-behavior capture, (2) feature construction, and (3) training/classi? cation. The ? st module serves to create a mouse-operation task, and to capture and interpret mouse-behavior data. The second module is used to extract holistic and procedural features to characterize mouse behavior, and to map the raw features into distance-based features by using various distance metrics. The third module, in the training phase, applies kernel PCA on the distance-based feature vectors to compute the predominant feature components, and then builds the user’s pro? le using a one-class classi? er. In the classi? cation phase, it determines the user’s identity using the trained classi? r in the distance-based feature eigenspace. B. Purpose and Contributions of This Paper This paper is a signi? cant extension of an earlier and much shorter version [40]. The main purpose and major contributions of this paper are summarized as follows: †¢ We address the problem of unintended side-effects of inconsistent experimental conditions and environmental variables by restricting usersâ€℠¢ mouse operations to a tightly-controlled environment. This isolates inherent behavioral characteristics as the principal factors in mouse behavior analysis, and substantially reduces the effects of external confounding factors. Instead of the descriptive statistics of mouse behaviors usually adopted in existing work, we propose newly-de? ned procedural features, such as movement speed curves, to characterize a user’s unique mouse-behavior characteristics in an accurate and ? ne-grained manner. These features could lead to a performance boost both in authentication accuracy and authentication time. †¢ We apply distance metrics and kernel PCA to obtain a distance-based eigenspace for ef? ciently representing the original mouse feature space.These techniques partially handle behavioral variability, and make our proposed approach stable and robust to variability in behavior data. †¢ We employ one-class learning methods to perform the user authentication task, so that the detection model is Fig. 1. Overview of approach. built solely on the data from the legitimate user. One-class methods are more suitable for mouse-dynamics-based user authentication in real-world applications. †¢ We present a repeatable and objective evaluation procedure to investigate the effectiveness of our proposed approach through a series of experiments.As far as we know, no earlier work made informed comparisons between different features and results, due to the lack of a standard test protocol. Here we provide comparative experiments to further examine the validity of the proposed approach. †¢ A public mouse-behavior dataset is established (see Section III for availability), not only for this study but also to foster future research. This dataset contains high-quality mouse-behavior data from 37 subjects. To our knowledge, this study is the ? rst to publish a shared mouse-behavior dataset in this ? eld. This study develops a mouse-dynamics-based user authenticat ion approach that performs user authentication in a short time while maintaining high accuracy. It has several desirable properties: 1. it is easy to comprehend and implement; 2. it requires no specialized hardware or equipment to capture the biometric data; 3. it requires only about 12 seconds of mouse-behavior data to provide good, steady performance. The remainder of this paper is organized as follows: Section II describes related work. Section III presents a data-collection process. Section IV describes the feature-construction process.Section V discusses the classi? cation techniques for mouse dynamics. Section VI presents the evaluation methodology. Section VII presents and analyzes experimental results. Section VIII offers a discussion and possible extensions of the current work. Finally, Section IX concludes. 18 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 II. BACKGROUND AND RELATED WORK In this section, we provide background on mouse- dynamics research, and various applications for mouse dynamics (e. g. , authentication versus intrusion detection).Then we focus on applying mouse dynamics to user authentication. A. Background of Mouse Dynamics Mouse dynamics, a behavioral biometric for analyzing behavior data from pointing devices (e. g. , mouse or touchpad), provides user authentication in an accessible and convenient manner [2]–[4], [8], [14]–[17], [19], [21], [22], [33], [34], [39]–[41], [45], [46]. Since Everitt and McOwan [14] ? rst investigated in 2003 whether users could be distinguished by the use of a signature written by mouse, several different techniques and uses for mouse dynamics have been proposed.Most researchers focus on the use of mouse dynamics for intrusion detection (sometimes called identity monitoring or reauthentication), which analyzes mouse-behavior characteristics throughout the course of interaction. Pusara and Brodley [33] proposed a reauthentication scheme using m ouse dynamics for user veri? cation. This study presented positive ? ndings, but cautioned that their results were only preliminary. Gamboa and Fred [15], [16] were some of the earliest researchers to study identity monitoring based on mouse movements.Later on, Ahmed and Traore [3] proposed an approach combining keystroke dynamics with mouse dynamics for intrusion detection. Then they considered mouse dynamics as a standalone biometric for intrusion detection [2]. Recently, Zheng et al. [46] proposed angle-based metrics of mouse movements for reauthentication systems, and explored the effects of environmental factors (e. g. , different machines). Yet only recently have researchers come to the use of mouse dynamics for user authentication (sometimes called static authentication), which analyzes mouse-behavior characteristics at particular moments.In 2007, Gamboa et al. [17] extended their approaches in identity monitoring [15], [16] into web-based authentication. Later on, Kaminsky e t al. [22] presented an authentication scheme using mouse dynamics for identifying online game players. Then, Bours and Fullu [8] proposed an authentication approach by requiring users to make use of the mouse for tracing a maze-like path. Most recently, a full survey of the existing work in mouse dynamics pointed out that mouse-dynamics research should focus on reducing authentication time and taking the effect of environmental variables into account [21]. B.User Authentication Based on Mouse Dynamics The primary focus of previous research has been on the use of mouse dynamics for intrusion detection or identity monitoring. It is dif? cult to transfer previous work directly from intrusion detection to authentication, however, because a rather long authentication period is typically required to collect suf? cient mouse-behavior data to enable reasonably accurate veri? cation. To our knowledge, few papers have targeted the use of mouse dynamics for user authentication, which will be the central concern of this paper. Hashia et al. [19] and Bours et al. 8] presented some preliminary results on mouse dynamics for user authentication. They both asked participants to perform ? xed sequences of mouse operations, and they analyzed behavioral characteristics of mouse movements to authenticate a user during the login stage. Distance-based classi? ers were established to compare the veri? cation data with the enrollment data. Hashia et al. collected data from 15 participants using the same computer, while Bours et al. collected data from 28 subjects using different computers; they achieved equal-error rates of 15% and 28% respectively.Gamboa et al. [17] presented a web-based user authentication system based on mouse dynamics. The system displayed an on-screen virtual keyboard, and required users to use the mouse to enter a paired username and pin-number. The extracted feature space was reduced to a best subspace through a greedy search process. A statistical model based on the Weibull distribution was built on training data from both legitimate and impostor users. Based on data collected from 50 subjects, the researchers reported an equal-error rate of 6. 2%, without explicitly reporting authentication time.The test data were also used for feature selection, which may lead to an overly optimistic estimate of authentication performance [18]. Recently, Revett et al. [34] proposed a user authentication system requiring users to use the mouse to operate a graphical, combination-lock-like GUI interface. A small-scale evaluation involving 6 subjects yielded an average false-acceptance rate and false-rejection rate of around 3. 5% and 4% respectively, using a distance-based classi? er. However, experimental details such as experimental apparatus and testing procedures were not explicitly reported. Aksari et al. 4] presented an authentication framework for verifying users based on a ? xed sequence of mouse movements. Features were extracted from nine move ments among seven squares displayed consecutively on the screen. They built a classi? er based on scaled Euclidean distance using data from both legitimate users and impostors. The researchers reported an equal-error rate of 5. 9% over 10 users’ data collected from the same computer, but authentication time was not reported. It should be noted that the above two studies were performed on a small number of users—only 6 users in [34], and 10 users in [4]—which may be insuf? ient to evaluate de? nitively the performance of these approaches. The results of the above studies have been mixed, possibly due to the realism of the experiments, possibly due to a lack of real differences among users, or possibly due to experimental errors or faulty data. A careful reading of the literature suggests that (1) most approaches have resulted in low performance, or have used a small number of users, but since these studies do not tend to be replicated, it is hard to pin the discr epancies on any one thing; (2) no research group provided a shared dataset.In our study, we control the experimental environment to increase the likelihood that our results will be free from experimental confounding factors, and we attempt to develop a simple and ef? cient user authentication approach based on mouse dynamics. We also make our data available publicly. III. MOUSE DATA ACQUISITION In this study, we collect mouse-behavior data in a controlled environment, so as to isolate behavioral characteristics as the principal factors in mouse behavior analysis. We offer here SHEN et al. USER AUTHENTICATION THROUGH MOUSE DYNAMICS 19 considerable detail regarding the conduct of data collection, because these particulars can best reveal potential biases and threats to experimental validity [27]. Our data set is available 1. A. Controlled Environment In this study, we set up a desktop computer and developed a Windows application as a uniform hardware and software platform for the coll ection of mouse-behavior data. The desktop was an HP workstation with a Core 2 Duo 3. 0 GHz processor and 2 GB of RAM.It was equipped with a 17 HP LCD monitor (set at 1280 1024 resolution) and a USB optical mouse, and ran the Windows XP operating system. Most importantly, all system parameters relating to the mouse, such as speed and sensitivity con? gurations, were ? xed. The Windows application, written in C#, prompted a user to conduct a mouse-operation task. During data collection, the application displayed the task in a full-screen window on the monitor, and recorded (1) the corresponding mouse operations (e. g. , mouse-single-click), (2) the positions at which the operations occurred, and (3) the timestamps of the operations.The Windows-event clock was used to timestamp mouse operations [28]; it has a resolution of 15. 625 milliseconds, corresponding to 64 updates per second. When collecting data, each subject was invited to perform a mouse-operations task on the same desktop computer free of other subjects; data collection was performed one by one on the same data-collection platform. These conditions make hardware and software factors consistent throughout the process of data collection over all subjects, thus removing unintended side-effects of unrelated hardware and software factors. B.Mouse-Operation Task Design To reduce behavioral variations due to different mouse-operation sequences, all subjects were required to perform the same sequence of mouse operations. We designed a mouse-operation task, consisting of a ? xed sequence of mouse operations, and made these operations representative of a typical and diverse combination of mouse operations. The operations were selected according to (1) two elementary operations of mouse clicks: single click and double click; and (2) two basic properties of mouse movements: movement direction and movement distance [2], [39].As shown in Fig. 2, movement directions are numbered from 1 to 8, and each of them is sel ected to represent one of eight 45-degree ranges over 360 degrees. In addition, three distance intervals are considered to represent short-, middle- and long-distance mouse movements. Table I shows the directions and distances of the mouse movements used in this study. During data collection, every two adjacent movements were separated by either a single click or a double click. As a whole, the designed task consists of 16 mouse movements, 8 single clicks, and 8 double clicks.It should be noted that our task may not be unique. However, the task was carefully chosen to induce users to perform a wide variety of mouse movements and clicks that were both typical and diverse in an individual’s repertoire of daily mouse behaviors. 1The mouse-behavior dataset is available from: http://nskeylab. xjtu. edu. cn/ projects/mousedynamics/behavior-data-set/. Fig. 2. Mouse movement directions: sector 1 covers all operations performed degrees and degrees. with angles between TABLE I MOUSE MO VEMENTS IN THE DESIGNED MOUSE-OPERATION TASK C.Subjects We recruited 37 subjects, many from within our lab, but some from the university at large. Our sample of subjects consisted of 30 males and 7 females. All of them were right-handed users, and had been using a mouse for a minimum of two years. D. Data-Collection Process All subjects were required to participate in two rounds of data collection per day, and waited at least 24 hours between collections (ensuring that some day-to-day variation existed within the data). In each round, each subject was invited, one by one, to perform the same mouse-operation task 10 times.A mouse-operation sample was obtained when a subject performed the task one time, in which she ? rst clicked a start button on the screen, then moved the mouse to click subsequent buttons prompted by the data-collection application. Additionally, subjects were instructed to use only the external mouse device, and they were advised that no keyboard would be needed. S ubjects were told that if they needed a break or needed to stretch their hands, they were to do so after they had accomplished a full round. This was intended to prevent arti? cially anomalous mouse operations in the middle of a task.Subjects were admonished to focus on the task, as if they were logging into their own accounts, and to avoid distractions, such as talking with the experimenter, while the task was in progress. Any error in the operating process (e. g. , single-clicking a button when requiring double-clicking it) caused the current task to be reset, requiring the subject to redo it. 20 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 TABLE II MOUSE DYNAMICS FEATURES Subjects took between 15 days and 60 days to complete data collection.Each subject accomplished 150 error-free repetitions of the same mouse-operation task. The task took between 6. 2 seconds and 21. 3 seconds, with an average of 11. 8 seconds over all subjects. The ? nal dataset contained 5550 samples from 37 subjects. IV. FEATURE CONSTRUCTION In this section, we ? rst extract a set of mouse-dynamics features, and then we use distance-measurement methods to obtain feature-distance vectors for reducing behavioral variability. Next, we utilize an eigenspace transformation to extract principal feature components as classi? er input. A.Feature Extraction The data collected in Section III are sequences of mouse operations, including left-single-clicks, left-double-clicks, and mouse-movements. Mouse features were extracted from these operations, and were typically organized into a vector to represent the sequence of mouse operations in one execution of the mouse-operation task. Table II summarizes the derived features in this study. We characterized mouse behavior based on two basic types of mouse operations—mouse click and mouse movement. Each mouse operation was then analyzed individually, and translated into several mouse features.Our study divi ded these features into two categories: †¢ Holistic features: features that characterize the overall properties of mouse behaviors during interactions, such as single-click and double-click statistics; †¢ Procedural features: features that depict the detailed dynamic processes of mouse behaviors, such as the movement speed and acceleration curves. Most traditional features are holistic features, which suf? ce to obtain a statistical description of mouse behavior, such as the mean value of click times. They are easy to compute and comprehend, but they only characterize general attributes of mouse behavior.In our study, the procedural features characterize in-depth procedural details of mouse behavior. This information more accurately re? ects the ef? ciency, agility and motion habits of individual mouse users, and thus may lead to a performance boost for authentication. Experimental results in Section VII demonstrate the effectiveness of these newly-de? ned features. B. Dis tance Measurement The raw mouse features cannot be used directly by a classi? er, because of high dimensionality and behavioral variability. Therefore, distance-measurement methods were applied to obtain feature-distance vectors and to mitigate the effects of these issues.In the calculation of distance measurement, we ? rst used the Dynamic Time Warping (DTW) distance [6] to compute the distance vector of procedural features. The reasons for this choice are that (1) procedural features (e. g. , movement speed curve) of two data samples are not likely to consist of the exactly same number of points, whether these samples are generated by the same or by different subjects; (2) DTW distance can be applied directly to measure the distance between the procedural features of two samples without deforming either or both of the two sequences in order to get an equal number of points.Next, we applied Manhattan distance to calculate the distance vector of holistic features. The reasons for th is choice are that (1) this distance is independent between dimensions, and can preserve physical interpretation of the features since its computation is the absolute value of cumulative difference; (2) previous research in related ? elds (e. g. , keystroke dynamics) reported that the use of Manhattan distance for statistical features could lead to a better performance [23]. ) Reference Feature Vector Generation: We established the reference feature vector for each subject from her training feature vectors. Let , be the training set of feature vectors for one subject, where is a -dimensional mouse feature vector extracted from the th training sample, and is the number of training samples. Consider how the reference feature vector is generated for each subject: Step 1: we computed the pairwise distance vector of procedural features and holistic features between all pairs of training feature vectors and .We used DTW distance to calculate the distance vector of procedural features for measuring the similarity between the procedural components of the two feature vectors, and we applied Manhattan distance to calculate the distance vector of holistic features . (1) where , and represents the procedural components of represents the holistic components. SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 21 Step 2: we concatenated the distance vectors of holistic features and procedural features together to obtain a distance vector for the training feature vectors and by (2) Step 3: we normalized vector: to get a scale-invariant feature nd sample covariance . Then we can obtain the mean of such a training set by (5) (6) (3) is the mean of all where pairwise distance vectors from the training set, and is the corresponding standard deviation. Step 4: for each training feature vector, we calculated the arithmetic mean distance between this vector and the remaining training vectors, and found the reference feature vector with minimum mean distance. (4) 2) Feature-Dis tance Vector Calculation: Given the reference feature vector for each subject, we then computed the feature-distance vector between a new mouse feature vector and the reference vector.Let be the reference feature vector for one subject; then for any new feature vector (either from the legitimate user or an impostor), we can compute the corresponding distance vector by (1), (2) and (3). In this paper, we used all mouse features in Table II to generate the feature-distance vector. There are 10 click-related features, 16 distance-related features, 16 time-related features, 16 speed-related features, and 16 acceleration-related features, which were taken together and then transformed to a 74-dimensional feature-distance vector that represents each mouse-operation sample. C.Eigenspace Computation: Training and Projection It is usually undesirable to use all components in the feature vector as input for the classi? er, because much of data will not provide a signi? cant degree of uniquene ss or consistency. We therefore applied an eigenspace-transformation technique to extract the principal components as classi? er input. 1) Kernel PCA Training: Kernel principal component analysis (KPCA) [37] is one approach to generalizing linear PCA to nonlinear cases using kernel methods. In this study, the purpose of KPCA is to obtain the principal components of the original feature-distance vectors.The calculation process is illustrated as follows: For each subject, the training set represents a set of feature-distance vectors drawn from her own data. Let be the th feature-distance vector in the training set, and be the number of such vectors. We ? rst mapped the measured vectors into the hyperdimensional feature space by the nonlinear mapping Here we centered the mapped point with the corresponding mean as . The principal components were then computed by solving the eigenvalue problem: (7) where and . Then, by de? ning a kernel matrix (8) we computed an eigenvalue problem for t he coef? ients is now solely dependent on the kernel function , that (9) For details, readers can refer to B. Scholkopf et al. [37]. Generally speaking, the ? rst few eigenvectors correspond to large eigenvalues and most information in the training samples. Therefore, for the sake of providing the principal components to represent mouse behavior in a low-dimensional eigenspace, and for memory ef? ciency, we ignored small eigenvalues and their corresponding eigenvectors, using a threshold value (10) is the accumulated variance of the ? st largest eigenwhere values with respect to all eigenvalues. In this study, was chosen as 0. 95 for all subjects, with a range from 0 to 1. Note that we used the same for different subjects, so may be different from one subject to another. Speci? cally, in our experiments, we observed that the number of principal components for different subjects varied from 12 to 20, and for an average level, 17 principal components are identi? ed under the threshold of 0. 95. 2) Kernel PCA Projection: For the selected subject, taking the largest eigenvalues and he associated eigenvectors, the transform matrix can be constructed to project an original feature-distance vector into a point in the -dimensional eigenspace: (11) As a result, each subject’s mouse behavior can be mapped into a manifold trajectory in such a parametric eigenspace. It is wellknown that is usually much smaller than the dimensionality of the original feature space. That is to say, eigenspace analysis can dramatically reduce the dimensionality of input samples. In this way, we used the extracted principal components of the feature-distance vectors as input for subsequent classi? ers. 22IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 V. CLASSIFIER IMPLEMENTATION This section explains the classi? er that we used, and introduces two other widely-used classi? ers. Each classi? er analyzes mouse-behavior data, and discriminates between a legitimate user and impostors. A. One-Class Classi? er Overview User authentication is still a challenging task from the pattern-classi? cation perspective. It is a two-class (legitimate user versus impostors) problem. In the scenario of mouse-dynamicsbased user authentication, a login user is required to provide the user name and to perform a speci? mouse-operation task which would be secret, like a password. Each user would choose her own mouse-operations task, and would not share that task with others. Thus, when building a model for a legitimate user, the only behavioral samples of her speci? c task are her own; other users’ (considered as impostors in our scenario) samples of this task are not readily available. In this scenario, therefore, an appropriate solution is to build a model based only on the legitimate user’s data samples, and use that model to detect impostors. This type of problem is known as one-class classi? ation [43] or novelty/anomaly detection [25], [26]. We thus focused our attention on this type of problem, especially because in a real-world situation we would not have impostor renditions of a legitimate user’s mouse operations anyway. B. Our Classi? er—One-Class Support Vector Machine Traditional one-class classi? cation methods are often unsatisfying, frequently missing some true positives and producing too many false positives. In this study, we used a one-class Support Vector Machine (SVM) classi? er, introduced by Scholkopf et al. [36], [38]. One-class SVMs have been successfully applied to a number of real-life classi? ation problems, e. g. , face authentication, signature veri? cation and keystroke authentication [1], [23]. In our context, given training samples belonging to one subject, , each sample has features (corresponding to the principal components of the feature-distance vector for that subject). The aim is to ? nd a hyperplane that separates the data points by the largest margin. To separ ate the data points from the origin, one needs to solve the following dual quadratic programming problem [36], [38]: the origin, and is the kernel function. We allow for nonlinear decision boundaries. Then the decision function 13) will be positive for the examples from the training set, where is the offset of the decision function. In essence, we viewed the user authentication problem as a one-class classi? cation problem. In the training phase, the learning task was to build a classi? er based on the legitimate subject’s feature samples. In the testing phase, the test feature sample was projected into the same high-dimensional space, and the output of the decision function was recorded. We used a radial basis function (RBF) in our evaluation, after comparative studies of linear, polynomial, and sigmoid kernels based on classi? ation accuracy. The SVM parameter and kernel parameter (using LibSVM [11]) were set to 0. 06 and 0. 004 respectively. The decision function would gen erate â€Å" † if the authorized user’s test set is input; otherwise it is a false rejection case. On the contrary, â€Å" † should be obtained if the impostors’ test set is the input; otherwise a false acceptance case occurs. C. Other Classi? ers—Nearest Neighbor and Neural Network In addition, we compared our classi? er with two other widely-used classi? ers, KNN and neural network [12]. For KNN, in the training phase, the nearest neighbor classi? r estimated the covariance matrix of the training feature samples, and saved each feature sample. In the testing phase, the nearest neighbor classi? er calculated Mahalanobis distance from the new feature sample to each of the samples in the training data. The average distance, from the new sample to the nearest feature samples from the training data, was used as the anomaly score. After multiple tests with ranging from 1 to 5, we obtained the best results with , detailed in Section VII. For the neural network, in the training phase a network was built with input nodes, one output node, and hidden nodes.The network weights were randomly initialized between 0 and 1. The classi? er was trained to produce a 1. 0 on the output node for every training feature sample. We trained for 1000 epochs using a learning rate of 0. 001. In the testing phase, the test sample was run through the network, and the output of the network was recorded. Denote to be the output of the network; intuitively, if is close to 1. 0, the test sample is similar to the training samples, and with close to 0. 0, it is dissimilar. VI. EVALUATION METHODOLOGY This section explains the evaluation methodology for mouse behavior analysis.First, we summarize the dataset collected in Section III. Next, we set up the training and testing procedure for our one-class classi? ers. Then, we show how classi? er performance was calculated. Finally, we introduce a statistical testing method to further analyze experimental results. (12) where is the vector of nonnegative Lagrangian multipliers to be determined, is a parameter that controls the trade-off between maximizing the number of data points contained by the hyperplane and the distance of the hyperplane from SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 23A. Dataset As discussed in Section III, samples of mouse-behavior data were collected when subjects performed the designed mouseoperation task in a tightly-controlled environment. All 37 subjects produced a total of 5550 mouse-operation samples. We then calculated feature-distance vectors, and extracted principal components from each vector as input for the classi? ers. B. Training and Testing Procedure Consider a scenario as mentioned in Section V-A. We started by designating one of our 37 subjects as the legitimate user, and the rest as impostors. We trained the classi? er and ested its ability to recognize the legitimate user and impostors as follows: Step 1: We trained the classi? er to b uild a pro? le of the legitimate user on a randomly-selected half of the samples (75 out of 150 samples) from that user. Step 2: We tested the ability of the classi? er to recognize the legitimate user by calculating anomaly scores for the remaining samples generated by the user. We designated the scores assigned to each sample as genuine scores. Step 3: We tested the ability of the classi? er to recognize impostors by calculating anomaly scores for all the samples generated by the impostors.We designated the scores assigned to each sample as impostor scores. This process was then repeated, designating each of the other subjects as the legitimate user in turn. In the training phase, 10-fold cross validation [24] was employed to choose parameters of the classi? ers. Since we used a random sampling method to divide the data into training and testing sets, and we wanted to account for the effect of this randomness, we repeated the above procedure 50 times, each time with independently selected samples drawn from the entire dataset. C. Calculating Classi? r Performance To convert these sets of classi? cation scores of the legitimate user and impostors into aggregate measures of classi? er performance, we computed the false-acceptance rate (FAR) and false-rejection rate (FRR), and used them to generate an ROC curve [42]. In our evaluation, for each user, the FAR is calculated as the ratio between the number of false acceptances and the number of test samples of impostors; the FRR is calculated as the ratio between the number of false rejections and the number of test samples of legitimate users.Then we computed the average FAR and FRR over all subjects. Whether or not a mouse-operation sample generates an alarm depends on the threshold for the anomaly scores. An anomaly score over the threshold indicates an impostor, while a score under the threshold indicates a legitimate user. In many cases, to make a user authentication scheme deployable in practice, minimizing the possibility of rejecting a true user (lower FRR) is sometimes more important than lowering the probability of accepting an impostor [46]. Thus we adjusted the threshold according to the FRR for the training data.Since calculation of the FRR requires only the legitimate user’s data, no impostor data was used for determining the threshold. Speci? cally, the threshold is set to be a variable ranging from , and will be chosen with a relatively low FRR using 10-fold cross validation on the training data. After multiple tests, we observe that setting the threshold to a value of 0. 1 yields a low FRR on average2. Thus, we show results with a threshold value of 0. 1 throughout this study. D. Statistical Analysis of the Results To evaluate the performance of our approach, we developed a statistical test using the half total error rate (HTER) and con? ence-interval (CI) evaluation [5]. The HTER test aims to statistically evaluate the performance for user authentication, which is de ? ned by combining false-acceptance rate (FAR) and falserejection rate (FRR): (14) Con? dence intervals are computed around the HTER as , and and are computed by [5]: (15) % % % (16) where NG is the total number of genuine scores, and NI is the total number of impostor scores. VII. EXPERIMENTAL RESULTS AND ANALYSIS Extensive experiments were carried out to verify the effectiveness of our approach. First, we performed the authentication task using our approach, and compared it with two widely-used classi? rs. Second, we examined our primary results concerning the effect of eigenspace transformation methods on classi? er performance. Third, we explored the effect of sample length on classi? er performance, to investigate the trade-off between security and usability. Two additional experiments are provided to compare our method with other approaches in the literature. A. Experiment 1: User Authentication In this section, we conducted a user authentication experiment, and compared our c lassi? er with two widely-used ones as mentioned in Section V-C. The data used in this experiment consisted of 5550 samples from 37 subjects.Fig. 3 and Table III show the ROC curves and average FARs and FRRs of the authentication experiment for each of three classi? ers, with standard deviations in parentheses. Table III also includes the average authentication time, which is the sum of the average time needed to collect the data and the average time needed to make the authentication decision (note that since the latter of these two times is always less than 0. 003 seconds in our classi? ers, we ignore it in this study). Our ? rst observation is that the best performance has a FAR of 8. 74% and a FRR of 7. 96%, obtained by our approach (one-class SVM).This result is promising and competitive, and the behavioral samples are captured over a much shorter period of time 2Note that for different classi? ers, there are different threshold intervals. For instance, the threshold interval fo r neural network detector is [0, 1], and for one. For uniform presentation, we mapped all of intervals class SVM, it is . to 24 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 TABLE IV HTER PERFORMANCE AND CONFIDENCE INTERVAL AT CONFIDENCE LEVELS DIFFERENT Fig. 3. ROC curves for the three different classi? rs used in this study: oneclass SVM, neural network, and nearest neighbor. TABLE III FARs AND FRRs OF USER AUTHENTICATION EXPERIMENT (WITH STANDARD DEVIATIONS IN PARENTHESES) information about mouse behavior, which could enhance performance. Finally, we conducted a statistical test, using the HTER and CI evaluation as mentioned in Section VI-D, to statistically evaluate the performance of our approach. Table IV summarizes the results of this statistical evaluation at different con? dence levels. The result shows that the proposed approach provides the lowest HTER in comparison with the other two classi? ers used in our study; the 95% con? ence interval lies at % %. B. Experiment 2: Effect of Eigenspace Transformation This experiment examined the effect of eigenspace-transformation methods on classi? er performance. The data used were the same as in Experiment 1. We applied a one-class SVM classi? er in three evaluations, with the inputs respectively set to be the original feature-distance vectors (without any transformations), the projection of feature-distance vectors by PCA, and the projection of feature-distance vectors by KPCA. Fig. 4 and Table V show the ROC curves and average FARs and FRRs for each of three feature spaces, with standard deviations in parentheses.As shown in Fig. 4 and Table V, the authentication accuracy for the feature space transformed by KPCA is the best, followed by the accuracies for feature spaces by PCA and the original one. Speci? cally, direct classi? cation in the original feature space (without transformations) produces a FAR of 15. 45% and FRR of 15. 98%. This result is not encouraging c ompared to results previously reported in the literature. However, as mentioned in Experiment 1, the samples may be subject to more behavioral variability compared with previous work, because previous work analyzed mouse behaviors over a longer period of observation.Moreover, we observe that the authentication results of % % by PCA, and % % by KPCA are much better than for direct classi? cation. This result is a demonstration of the effectiveness of the eigenspace transformation in dealing with variable behavior data. Furthermore, we ? nd that the performance of KPCA is slightly superior to that of PCA. This may be due to the nonlinear variability (or noise) existing in mouse behaviors, and KPCA can reduce this variability (or noise) by using kernel transformations [29].It is also of note that the standard deviations of FAR and FRR based on the feature space transformed by KPCA and PCA are smaller than those of the original feature space (without transformations), indicating that th e eigenspace-transformation technique enhances the stability and robustness of our approach. compared with previous work. It should be noted that our result does not yet meet the European standard for commercial biometric technology, which requires near-perfect accuracy of 0. 001% FAR and 1% FRR [10]. But it does demonstrate that mouse dynamics could provide valuable information in user authentication tasks.Moreover, with a series of incremental improvements and investigations (e. g. , outlier handling), it seems possible that mouse dynamics could be used as, at least, an auxiliary authentication technique, such as an enhancement for conventional password mechanisms. Our second observation is that our approach has substantially better performance than all other classi? ers considered in our study. This may be due to the fact that SVMs can convert the problem of classi? cation into quadratic optimization in the case of relative insuf? ciency of prior knowledge, and still maintain hig h accuracy and stability.In addition, the standard deviations of the FAR and FRR for our approach are much smaller than those for other classi? ers, indicating that our approach may be more robust to variable behavior data and different parameter selection procedures. Our third observation is that the average authentication time in our study is 11. 8 seconds, which is impressive and achieves an acceptable level of performance for a practical application. Some previous approaches may lead to low availability due to a relatively-long authentication time. However, an authentication time of 11. seconds in our study shows that we can perform mouse-dynamics analysis quickly enough to make it applicable to authentication for most login processes. We conjecture that the signi? cant decrease of authentication time is due to procedural features providing more detailed and ? ne-grained SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 25 TABLE VI FARs AND FRRs OF DIFFERENT SAMPLE LENGTH S Fig. 4. ROC curves for three different feature spaces: the original feature space, the projected feature space by PCA, and the projected feature space by KPCA.TABLE V FARs AND FARs FOR THREE DIFFERENT FEATURE SPACES (WITH STANDARD DEVIATIONS IN PARENTHESES) the needs of the European Standard for commercial biometric technology [10]. We ? nd that after observing 800 mouse operations, our approach can obtain a FAR of 0. 87% and a FRR of 0. 69%, which is very close to the European standard, but with a corresponding authentication time of about 10 minutes. This long authentication time may limit applicability in real systems. Thus, a trade-off must be made between security and user acceptability, and more nvestigations and improvements should be performed to secure a place for mouse dynamics in more pragmatic settings. D. Comparison User authentication through mouse dynamics has attracted growing interest in the research community. However, there is no shared dataset or baseline algor ithm for measuring and determining what factors affect performance. The unavailability of an accredited common dataset (such as the FERET database in face recognition [32]) and standard evaluation methodology has been a limitation in the development of mouse dynamics.Most researchers trained their models on different feature sets and datasets, but none of them made informed comparisons among different mouse feature sets and different results. Thus two additional experiments are offered here to compare our approach with those in the literature. 1) Comparison 1: Comparison With Traditional Features: As stated above, we constructed the feature space based on mouse clicks and mouse movements, consisting of holistic features and procedural features. To further examine the effectiveness of the features constructed in this study, we provide a comparative experiment. We chose the features used by Gamboa et al. 17], Aksari and Artuner [4], Hashia et al. [19], Bours and Fullu [8], and Ahmed a nd Traore [2], because they were among the most frequently cited, and they represented a relatively diverse set of mouse-dynamics features. We then used a one-class SVM classi? er to conduct the authentication experiment again on our same dataset with both the feature set de? ned in our study, and the feature sets used in other studies. Hence, the authentication accuracies of different feature sets can be compared. Fig. 5 and Table VII show the ROC curves and average FARs and FRRs for each of six feature sets, with standard deviations in parentheses.We can see that the average error rates for the feature set from our approach are much lower than those of the feature sets from the literature. We conjecture that this may be due to the procedural features providing ? ne-grained information about mouse behavior, but they may also be due, in part, to: (1) partial adoption of features de? ned in previous approaches C. Experiment 3: Effect of Sample Length This experiment explored the effe ct of sample length on classi? er performance, to investigate the trade-off between security (authentication accuracy) and usability (authentication time).In this study, the sample length corresponds to the number of mouse operations needed to form one data sample. Each original sample consists of 32 mouse operations. To explore the effect of sample length on the performance of our approach, we derived new datasets with different sample lengths by applying bootstrap sampling techniques [13] to the original dataset, to make derived datasets containing the same numbers of samples as the original dataset. The new data samples were generated in the form of multiple consecutive mouse samples from the original dataset. In this way, we considered classi? r performance as a function of the sample length using all bootstrap samples derived from the original dataset. We conducted the authentication experiment again (using one-class SVM) on six derived datasets, with and 800 operations. Table VI shows the FARs and FRRs at varying sample lengths, using a one-class SVM classi? er. The table also includes the authentication time in seconds. The FAR and FRR obtained using a sample length of 32 mouse operations are 8. 74% and 7. 96% respectively, with an authentication time of 11. 8 seconds. As the number of operations increases, the FAR and FRR drop to 6. 7% and 6. 68% for the a data sample comprised of 80 mouse operations, corresponding to an authentication time of 29. 88 seconds. Therefore, we may conclude that classi? er performance almost certainly gets better as the sample length increases. Note that 60 seconds may be an upper bound for authentication time, but the corresponding FAR of 4. 69% and FRR of 4. 46% are still not low enough to meet 26 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 Fig. 5. ROC curves for six different feature sets: the feature set in our study, and the features sets in other studies.RESULTS OF TABLE VII CO MPARISON WITH SOME TRADITIONAL FEATURES (WITH STANDARD DEVIATIONS IN PARENTHESES) Note that this approach [2] is initially applied to intrusion detection, and we extracted parts of features closely related to mouse operations in our dataset. The reason for this decision is that we want to examine whether the features employed in intrusion detection can be used in user authentication. because of different data-collection environments; (2) using different types of thresholds on the anomaly scores; (3) using less enrollment data than was used in previous experiments.The improved performance based on using our features also indicates that our features may allow more accurate and detailed characterization of a user’s unique mouse behavior than was possible with previously used features. Another thing to note from Table VII is that the standard deviations of error rates for features in our study are smaller than those for traditional features, suggesting that our features might be more stable and robust to variability in behavior data. One may also wonder how much of the authentication accuracy of our approach is due to the use of procedural features or holistic features.We tested our method using procedural features and holistic features separately, and the set of procedural features was the choice that proved to perform better. Specifically, we observe that the authentication accuracy of % % by using the set of procedural features is much better than for the set of holistic features, which have a FAR of 19. 58% and a FRR of 17. 96%. In combination with the result when using all features, it appears that procedural features may be more stable and discriminative than holistic features, which suggests that the procedural features contribute more to the authentication accuracy.The results here only provide preliminary comparative results and should not be used to conclude that a certain set of mouse features is always better than others. Each feature set has it s own unique advantages and disadvantages under different conditions and applications, so further evaluations and comparisons on more realistic and challenging datasets are needed. 2) Comparison 2: Comparison With Previous Work: Most previous approaches have either resulted in poor performance (in terms of authentication accuracy or time), or have used data of limited size.In this section, we show a qualitative comparison of our experimental results and settings against results of previous work (listed in Table VIII). Revett et al. [34] and Aksari and Artuner [4] considered mouse dynamics as a standalone biometric, and obtained an authentication accuracy of ERR around 4% and 5. 9% respectively, with a relatively-short authentication time or small number of mouse operations. But their results were based on a small pool of users (6 users in [34] and 10 users in [4]), which may be insuf? ient to obtain a good, steady result. Our study relies on an improved user authentication methodolo gy and far more users, leading us to achieve a good and robust authentication performance. Ahmed and Traore [2] achieved a high authentication accuracy, but as we mentioned before, it might be dif? cult to use such a method for user authentication since the authentication time or the number of mouse operations needed to verify a user’s identity is too high to be practical for real systems. Additionally, Hashia et al. 19] and Bours and Fulla [8] could perform user authentication in a relatively-short time, but they reported unacceptably high error rates (EER of 15% in [19], and EER of 26. 8% in [8]). In our approach we can make an authentication decision with a reasonably short authentication time while maintaining high accuracy. We employ a one-class classi? er, which is more appropriate for mouse-dynamics-based user authentication. As mentioned in Experiment 3, we can make an authentication decision in less than 60 seconds, with corresponding error rates are FAR of 4. 9% and FRR of 4. 46%. Although this result could be improved, we believe that, at our current performance level, mouse dynamics suf? ce to be a practical auxiliary authentication mechanism. In summary, Comparison 1 shows that our proposed features outperform some traditional features used in previous studies, and may be more stable and robust to variable behavior data. Comparison 2 indicates that our approach is competitive with existing approaches in authentication time while maintaining high accuracy.More detailed statistical studies on larger and more realistic datasets are desirable for further evaluations. VIII. DISCUSSION AND EXTENSION FOR FUTURE WORK Based on the ? ndings from this study, we take away some messages, each of which may suggest a trajectory for future work. Additionally, our work highlights the need for shared data and resources. A. Success Factors of Our Approach The presented approach achieved a short authentication time and relatively-high accuracy for mouse-dynami cs-based user SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 27 TABLE VIII COMPARISON WITH PREVIOUS WORKAuthentication time was not explicitly reported in [4], [8], [17]; instead, they required the user to accomplish a number of mouse operations for each authentication (15 clicks and 15 movements for [17]; 10 clicks and 9 movements for [4]; 18 short movements without pauses for [8]). Authentication time was not explicitly stated in [2]; however, it can be assumed by data-collection progress. For example, it is stated in [2] that an average of 12 hours 55 minutes of data were captured from each subject, representing an average of 45 sessions. We therefore assume that average session length is 12. 5 60/45 17. 22 minutes 1033 seconds. authentication. However, it is quite hard to point out one or two things that may have made our results better than those of previous work, because (1) past work favored realism over experimental control, (2) evaluation methodologies were incons istent among previous work, and (3) there have been no public datasets on which to perform comparative evaluations. Experimental control, however, is likely to be responsible for much of our success. Most previous work does not reveal any particulars in controlling experiments, while our work is tightly controlled.We made every effort to control experimental confounding factors to prevent them from having unintended in? uence on the subject’s recorded mouse behavior. For example, the same desktop computer was used for data collection for all subjects, and all system parameters relating to the mouse were ? xed. In addition, every subject was provided with the same instructions. These settings suggest strongly that the differences in subjects were due to individually detectable mouse-behavior differences among subjects, and not to environmental variables or experimental conditions.We strongly advocate the control of potential confounding factors in future experiments. The reaso n is that controlled experiments are necessary to reveal causal connections among experimental factors and classi? er performance, while realistic but uncontrolled experiments may introduce confounding factors that could in? uence experimental outcomes, which would make it hard to tell whether the results of those evaluations actually re? ect detectable differences in mouse behavior among test subjects, or differences among computing environments.We had more subjects (37), more repetitions of the operation task (150), and more comprehensive mouse operations (2 types of mouse clicks, 8 movement directions, and 3 movement distance ranges) than most studies did. Larger subject pools, however, sometimes make things harder; when there are more subjects there is a higher possibility that two subjects will have similar mouse behaviors, resulting in more classi? cation errors. We proposed the use of procedural features, such as the movement speed curve and acceleration curve, to provide mor e ? egrained information about mouse behavior than some traditional features. This may allow one to accurately describe a user’s unique mouse behavior, thus leading to a performance improvement for mouse-dynamics-based user authentication. We adopted methods for distance measurement and eigenspace transformation for obtaining principal feature components to ef? ciently represent the original mouse feature space. These methods not only overcome within-class variability of mouse behavior, but also preserve between-class differences of mouse behavior. The improved authentication accuracies demonstrate the ef? acy of these methods. Finally, we used a one-class learning algorithm to perform the authentication task, which is more appropriate for mousedynamics-based user authentication in real applications. In general, until there is a comparative study that stabilizes these factors, it will be hard to be de? nitive about the precise elements that made this work successful. B. Oppor tunities for Improvement While previous studies showed promising results in mouse dynamics, none of them have been able to meet the requirement of the European standard for commercial biometric technology.In this work, we determined that mouse dynamics may achieve a pragmatically useful level of accuracy, but with an impractically long authentic

Anthropology 101 Research Paper

Komron Sabbagh Prof. Rowe Anthropology 101 March 25, 2013 Elderly Human â€Å"Y† Chromosome The unearthing and examination of a tremendously infrequent African American â€Å"Y† chromosome goes back in time with regards to the most recent common ancestor for the â€Å"Y† chromosome ancestry to 338,000 years ago. This period exists even older than the age of the most eldest known structurally contemporary human fossils.University of Arizona geneticists have revealed the most ancient known hereditary subdivision of the human â€Å"Y† chromosome — the genetic factor which determines the male sex. The new differing pedigree, which was discovered in a male human being who presented his DNA to â€Å"Family Tree DNA†, a company which concentrates on DNA investigation to locate family roots, separated from the â€Å"Y† chromosome tree before the very first presence of physically current individuals in the record of fossils. These effects are p rinted in the American Journal of Human Genetics.Michael Hammer of the University of Arizona’s department of ecology and evolutionary biology stated that, â€Å"Our analysis indicates this lineage diverged from previously known Y chromosomes about 338,000 ago, a time when anatomically modern humans had not yet evolved. This pushes back the time the last common Y chromosome ancestor lived by almost 70 percent. † Dissimilar to the added human chromosomes, the common â€Å"Y† chromosome doesn’t barter heritable information with other chromosomes; this makes it a lot more straightforward and scientists can truly discover familial associations amid modern ancestries.If two â€Å"Y† chromosomes transmit an identical mutation, it is most likely since they divide a communal forefather at some particular period in the precedent. The further mutations which differ amongst two Y chromosomes, the farther back in history the mutual antecedent existed. Initially , a DNA sample acquired from an African American existing in South Carolina was succumbed to the National Geographic Genographic Project. When none of the hereditary indicators used to dispense ancestries to identified â€Å"Y† chromosome consortiums were found, the DNA sample was guided to â€Å"Family Tree DNA† for organizing.Fernando Mendez, who is a postdoctoral scholar in Hammer's laboratory, controlled the attempt to investigate the DNA sequence, which comprised of over 240,000 base pairs of the Y chromosome. Hammer claimed that â€Å"the most striking feature of this research is that a consumer genetic testing company identified a lineage that didn't fit anywhere on the existing Y chromosome tree, even though the tree had been constructed based on perhaps a half-million individuals or more. Nobody expected to find anything like this. At around 300,000 years ago; this was the period of time in which the Neanderthals are thought to have fragmented from the famili al human descent. It was not until more than 100,000 years in the future that functionally recent humans seem to be in the fossil record. They vary from the more antiquated forms by a more frivolously constructed skeleton; this includes a lesser face pushed underneath a tall forehead, the lack of a cranial ridge and slighter chins. Hammer stated that the recently exposed â€Å"Y† chromosome dissimilarity is tremendously occasional.Through the use of great databank explorations, his group ultimately was capable of discovering a comparable chromosome in the Mbo, which is a populace living in a petite region of western Cameroon in the sub-Saharan part of Africa. â€Å"This was surprising because previously the most diverged branches of the Y chromosome were found in traditional hunter-gatherer populations such as Pygmies and the click-speaking KhoeSan, who are considered to be the most diverged human populations living today.Instead, the sample matched the Y chromosome DNA of 1 1 men, who all came from a very small region of western Cameroon,† Hammer explains. â€Å"And the sequences of those individuals are variable, so it's not like they all descended from the same grandfather. † Hammer restraints against prevalent notions of â€Å"mitochondrial Eve† or â€Å"Y chromosome Adam† which propose that all of humanity was derived from precisely one couple of individuals that lived at a particular point in human biological evolution. There has been too much emphasis on this in the past,† Hammer says. â€Å"It is a misconception that the genealogy of a single genetic region reflects population divergence. Instead, our results suggest that there are pockets of genetically isolated communities that together preserve a great deal of human diversity. † Nevertheless, Hammer explains that, â€Å"It is likely that other divergent lineages will be found, whether in Africa or among African-Americans in the U. S. and that some of t hese may further increase the age of the Y chromosome ree. † He further clarifies: â€Å"There has been a lot of hype with people trying to trace their Y chromosome to different tribes, but this individual from South Carolina can say he did it. † The investigation originated by the mutual labors of a private business, the â€Å"Family Tree DNA,† the struggles of a resident scientist, Bonnie Schrack, and the research proficiencies at the UA. â€Å"Human Y Chromosome Much Older Than Previously Thought. † ScienceDaily. ScienceDaily, 04 Mar. 2013. Web. 26 Mar. 2013.

Byzantine Empire Essay Example | Topics and Well Written Essays - 2500 words

Byzantine Empire - Essay Example They also had a lot to do with installations and depositions of emperors (Sewter/Psellus 1997). To understand them better, it is important to note that these three Empresses lived at the time following the birth of Christendom, from about 770 to the middle of the second century, when the Roman Empire had its main base and 'headquarters' in Byzantium, a place we now know as Istanbul, in Turkey. It was a strategic place, in a prime position on the coast of the important Mediterranean Sea (Sewter/Psellus 1979). With part of its area firmly in Europe, and the other at the gateway to the Holy Lands (known now as the Middle East) and Asia, this area could dominate in terms of wealth, politics and power. Syria, whose cities of Aleppo, Damascus and Antioch were centers of trade and knowledge, was so close by that the men - and the important women - of this age and time had a lot of resources at their disposal, both in material wealth and in clever advisors, and this enabled them to mark history with their own names in all matters that had to do with culture, the military and without any doubt, the economy. Chronologically, the first of these women, Irene, was a powerful monarch in her own right, and it is an indication of her attitude that she chose to call herself by the masculine term 'Basileus' or Emperor (Garland 1999). After being expelled from the marriage-bed because of hiding icons when the Emperor had banned them, she became involved in a number of conspiracies. Intelligent and wily - probably because of her doubtful social status before she was married - she wielded power from behind the scenes, coming into her full might when she became regent for her son on the death of her husband Leo IV. Her son was to become Constantine VI, but while he was younger, his mother took advantage of the position of regent to enforce her beliefs in Christianity and the Pope. She used her wiles to promote some men and get rid of others. A famous action of hers was the idea of ordaining those who threatened the throne. Being priests disqualified them from being candidates (Sewter/Psellus ibid). No stranger to intrigue, she made her son extremely uncomfortable with her exploits when he came into power. There are several important political events of her time that have Irene's unmistakable signature: she liked underhanded dealings and plots. But, ironically, she became most famous for restoring worship of icons and other religious images. (Garland 1999). This seems to indicate that she felt her power came from her faith and that it absolved her of a lot of unethical or improper dealings. She deposed her own son and had him exiled, after which she ruled in her own right for five years. This grand conspiracy caused deep factions in the Church and the empire court. When she had her son's eyes gouged out, which killed him, people believed the heavens were angry, because the sky darkened for a number of days (Garland 1999). People believed she had enough power to affect more than just politics. She was revered just like a saint after her death, probably because of her political power that restored worship to those who wanted it. She has never been canonized. Irene was ultimately taken off the throne and had

Write an essay that discusses what James Tate,Robert Bly, Billy

Write an that discusses what James Tate,Robert Bly, Billy Collins,and Mattew Dickman think a good poem is.what does a good poem do how can we evaluate a poem properly - Essay Example Writing a poem that has much significance requires time. Writing a poem is not simple because in order for one to write a poem, they should know how to twist their words, use jargons and know how to express their own feelings into the poem. Individuals have in the past praised poems for the power they possess and signify, while others have not been in support of them simply because they cannot understand the concept behind them (Ross, 303). Certain scholars described what a good poem is supposed to be, what it was supposed to signify and how good poems are evaluated. The scholars; James Tate, Robert Bly, Collins, Matthew Dickman described a good poem as a piece of literature that individuals are able to relate with. A good poem incorporates the aspect of culture, which is noteworthy since culture is what every single being follows. A poem that significantly speaks about the cultures of people is simply a good poem since readers can relate it to the life they live. A good poem relates to the reader in a sentimental way. There is sentimental value between the reader and the poem. From the metaphors used to the complex language structure, a good poem is designed to show insight or certain revelation. It is supposed to show significance to the reader; from the way the words rhyme to the way they are twisted. A good poem does not necessarily reflect the writers’ own personal experience. It can just be a fantasy, yet it makes sense and in a way relates to the reader (Ross, 307). Most writers write poems because they have a passion and derive a certain level of satisfaction from their work. They write to let their feelings known, to be recognized as people who relate with words. A good poem is a piece of literary writing that rejuvenates the reader. The primary goal of a good poem is to focus all the attention of the reader into the poem. It should not be channeled towards

The Role Of Public Relation In Crisis Management in the Oil & Gas Research Paper

The Role Of Public Relation In Crisis Management in the Oil & Gas industry - Research Paper Example Whenever a crisis happens, the management of an organization needs to proceed in a manner that would guarantee the most effective coordination of the three groups, which would ensure that public relations is used in the management of the crisis. The role of public relations in the management of crisis in oil and gas industry has been portrayed in a number of crises with some companies managing the crisis poorly and damaging the image of the company while others using public relations strategies to improve the company’s image. The oil and gas industry has experienced a number of crises with some generating good public response and others attracting a public outrage and rendering the company almost bankrupt. The response to the crisis of a toxic gas release, which happened on December 1984 at a Union Carbide Chemical Factory or Plant located in Bhopal, India killing over 2000 (3800) people formed one of the most effectively managed crisis in history. The team managing this crisis consisted of ten of the executives and managers of Carbide headed by the C.E.O and worked for several months in coordinating the management, operation, and communication response to the industrial accident. The public relation methods that were utilized in managing this crisis included crisis communication, consumer public relations, internal communication, and government relations. According to a report by Jackson Browning (1993), the then vice president of Union Carbide Corporation in charge of safety, health, and environmental programs, the team held its first press conference that took very few minutes. In the press conference, the team acknowledged that the serious disaster had happened in a factory owned by the Union Carbide where they had a 50.9% share. The team explained to the press some of the immediate measur5es that they were already undertaking in addressing the crisis. The company had daily briefings where they would answer

Business Employment Law Case Study Example | Topics and Well Written Essays - 1000 words

Business Employment Law - Case Study Example Notably, Mr. Dunlap presented claims that showed the aspects of priority given to the white over the black people, irrespective of them having better qualifications and work experiences than the white people. However, Mr. Dunlap failed to prove the case regarding disparate impact. The case later proceeded to the U.S. Court of Appeal with the legal issue being, to establish whether Dunlap fulfilled the burden of proof in the case, in addition to establishing the correctness of the District Court findings. The Court of Appeal confirmed the decisions of the District Court in the case, as they cited the insufficient evidence for disparate treatment. They also upheld the decision on awards for the damages and fees. However, the Court of Appeal reversed the verdict on disparate impact.Why the plaintiff’s disparate, impact claim failedMr. Dunlap suit alleged that TVA manipulated the process of selection, causing the disparate impact on the minority candidates. He alleged he was a vic tim of intentional disparate treatment that both violate the Title VII. In the analysis, the impact theory expects the employee or job applicant to ensure they demonstrate that an apparent employment practice affects a given group harshly. In addition, that the employment practice favors the other side without justification. The countering side, that is the employer, in this case, TVA should show that the manifest procedure relates to the employment process, an argument called â€Å"business necessity† justification.

Same sexual-marriage Essay Example | Topics and Well Written Essays - 1500 words

Same sexual-marriage - Essay Example First, on the historical ground, it is noted that the first recoded statement of people with the same biological sex who fall in love was in U.S, the University of Minnesota. Two students by the name John Richard and his lover McConnel Micheal fell in love, and when the pleasure grew to uncontrollable situation, they matched to county district court, for a request of marriage (Burns 120). This dramatic event occurred in 1970, particularly the eighth day of the fifth month. Unfortunately, it is in tabulation that the Court- Clerk by then Mr. Nelson Gerald declined the application, on the ground that both the applicants were men. This marked the beginning of  Ã‚  unending journey of love. With the knowledge they had concerning Minnesota law, the two applicants went ahead to sue Nelson, who was the Clerk by then handling the case, arguing that Minnesota laws do not mention anything to do with gender. In favor of Nelson, the trial court was not impressed with the claim and so they agre ed with Nelson. Nonetheless, the two lovers went ahead to seek the Supreme Courts intervention (Edwards 232). Still, since it was the first thing to be heard in America, the couple faced a rebuff again. However, the journey had been ignited. After several rejection and constitutional bans on the same sexual marriage, the Netherlands opened up. It is documented that in 1979, the Netherland country loosened up to the extent of adopting the unregistered cohabitation. Therefore, couple of the same sex could cohabitate, although not under a registered permit. This further, was forced to enter into scheme on the ground of being a civil status in rent law. Consequently, Netherlands was the first country to embrace same sexual marriage, and that they permitted the couples to apply for limited rights on the same. This wave moved across the world

Land Law Essay Example | Topics and Well Written Essays - 3000 words - 3

Land Law - Essay Example In addition, equitable interests bound persons other than bona fide purchases of the estate for value without any notice of equitable rights3. However, Law of Property Act 2002 outlines certain legal rights such as leases for more than seven years that require registration and that will bind the purchaser of the land. Covenants, easements and estate contracts need registration; otherwise, the purchaser will not be bound regardless of whether he had knowledge of such interests. However, the right of beneficiaries under trust is overreaching thus is subject to doctrine of notice4. Estates and tenures stem from common law that dominated the early English law system that eventually evolved to Royal courts in terms of common pleas and exchequer. However, writs of the courts led to injustice in certain cases and principles of equity emerged based on conscience. Equity would prevail over common law in cases of conflict. For instance, common law courts refused to recognize the right of beneficiaries under trust land since it is only the trustees who had legal rights to the land unlike courts of equity that fully recognized the right of beneficiaries to the property5. In this case, equitable rights were not enforceable against a bona fide purchaser of a legal estate for value without any notice of any other attached claim to the estate. On the other hand, common law acts in rem and is enforceable against anybody ‘good against the whole world’ on all legal estates and interests6. According to land law, a bona fide purchaser for value is an innocent party who purchases property without any notice of any other party’s claim to the land. The bona fide purchaser must acquire the land for value rather than being a beneficiary to the land. In this case, the purchaser can acquire title to the land despite the competing claim from other interested parties7. A purchaser

Japanese history - The Meiji Restoration Essay Example | Topics and Well Written Essays - 500 words

Japanese history - The Meiji Restoration - Essay Example These Samurai were motivated by the current state of the country including threat of encroachment from foreigners and emergent domestic problems. They adopted the fukoku-kyohei slogan (â€Å"Wealthy country and strong arms†) and pursued after creating a nation-state that could stand equal among the Western powers. The new government, as deduced from the 1868 Charter Oath, sought to dismantle the aged feudal regime (Devine, 51). In mid-1870s, restoration leaders, acting under the Emperor’s name, faced such steep opposition in carrying out the restoration changes. SaigÃ…  Takamori lead the famous disgruntled samurai in rebelling against the government which were later, with great difficulty, repressed by the formed army and in 1880’s, peasants who had grown distrustful of the newly formed regiment, joined in the revolts bringing it to its peak. This turmoil was halfway dissolved by s call from a Rights movement that was gaining popularity although it was mainly influenced by liberal western ideas. They advocated the formation of a constitutionally-based government with deliberative assemblies (Devine, 54). In 1881, the government responded by issuing a statement that promised a constitution by year 1890 and in 1886, constitution formation started after the formation of a cabinet system in 1885. By year 1889, a constitution was promulgated to the people as a gift from Meiji Social and economic changes were concurrent with the political changes that were already taking place during this era. Agriculture was the primary drive for the economy of Japan (Schirokauer, Lurie, and Gay). However, the Meiji government was working towards industrialization and hence directed developments in communications, transportation and strategic industries. Railroads were built, telegraphs linking all the main cities and private sectors received government support thru funds and European-like banking system. They relied on and heavily promoted western technology and

Kinds of Musical Instruments Essay Example for Free

Kinds of Musical Instruments Essay Trumpet A soprano brass wind instrument consisting of a long metal tube looped once and ending in a flared bell, the modern type being equipped with three valves for producing variations in pitch. Trombone A brass instrument consisting of a long cylindrical tube bent upon itself twice, ending in a bell-shaped mouth, and having a movable U-shaped slide for producing different pitches. French Horn A valved brass wind instrument that produces a mellow tone from a long narrow tube that is coiled in a circle before ending in a flaring bell. Tuba A large, valved, brass wind instrument with a bass pitch. A reed stop in an organ, having eight-foot pitch. Euphonium A brass wind instrument similar to the tuba but having a somewhat higher pitch and a mellower sound. Flugelhorn The could produce only the natural harmonics flugelhorn is a valved bugle developed in Germany. It has a conical bore. The bugle had no valves and therefore of the tube. Percussion instruments DRUM SET The first drum sets were put together in the late 1800s sometime after the invention of the bass drum pedal. This invention made it possible for one person to play several percussion instruments (snare drum, bass drum, and cymbals) at one time Bass Drum This drum is the largest member of the set and is played by using a foot pedal attached to a beater which then strikes the drum head. This drum produces a low deep sound. Snare Drum This shallow, cylindrical drum produces a sound that is very distinctive to the drum (higher in pitch than the bass drum). The snares,  which are bands of metal wires, are pulled across the bottom head of the drum. This produces a buzzing or snapping sound when the drum is struck using a variety of techniques. Bell Bells can be made from various materials including clay, glass or metal. It also ranges in shape and size. It may be played by lightly shaking it as in hand bells or by striking it using a metal or wooden striker or mallet. Bongo Drum Another type of drum that is mostly used in world music is the bongo drum. Bongos are played by striking the fingertips and/or the heel of the hand on the drumhead. Castanet For some reason I think of chestnuts when I hear the word castanets. True enough the word castanet was derived from the Spanish word castana meaning chestnuts. Castanets belong to the clapper family of percussion instruments. Conga Drum A conga drum is another type of percussion instrument belonging to the drum family. It is shaped somewhat like a barrel and is played the same way as the bongo drum. Conga also refers to a form of dance of Afro-Cuban origin. A perfect example is the song Conga by Gloria Estefan. Cymbal The player holds the strap attached to each cymbal and brushes it against each other or clash it together. It can either be held horizontally or vertically and played either loudly or softly depending on the music. Glockenspiel Glockenspiels have tuned steel bars or tubes which are struck by the musician using two beaters. The beaters may be made from metal, wood or rubber. Gong Remember that show on NBC during the 70s hosted by Chuck Barris? It was called The Gong Show and its an amateur talent show where the gong was sounded to signal that a contestant was eliminated. Read more about the gong. Maracas The maracas is one of the easiest musical instruments to play; you just need to have a sense of rhythm, timing and a flair for shaking. Maracas are made in various materials including plastic and wood and it ranges from the very simple to the most intricate designs. Metallophone Generally, metallophones differ from xylophones because the tuned bars which are struck with a mallet are made of metal, hence the name metallophones. There are many different kinds of metallophones; here we will focus on those which are used by Indonesian gamelan orchestras. TRIANGLE The triangle is another commonly used percussion instrument. The instrument is made by bending a steel rod into a triangle shape with an opening at one corner. It is suspended by a string and struck with a steel beater to produce a tone. The instrument has been used in Europe since the 14th century. XYLOPHONE The xylophone is a mallet percussion instrument. It consists of a set of graduated wooden bars which are hit with mallets to produce a tone. Xylophones were used in Southeast Asia during the 1300s and spread to Africa, Latin America, and Europe. Woodwind Instruments Saxophone The saxophone is a member of the reed -sounded wind instruments. In its construction, it combines the single reed and mouthpiece of the clarinet, a metal body, and a widened version of the conical bore of the oboe. Bassoon The bassoon is a double reed instrument. It is made up of about eight feet of cylidrical wood tubing. There are four joints in the bassoon: the bass joint, the tenor join, the double joint, and the bell joint. Clarinet The clarinet, a member of the woodwind family, usually consists of a long tube with a mouthpiece at one end and a bell-shaped opening at the other end. Usually made of wood, the clarinet has tone holes that are covered by small metal levers Oboe The oboe is the smallest and highest pitched double reed instrument. It has a cylindrical wooden body with keys along the length of its body. English Horn The English Horn is part of the oboe family. It is also called an alto oboe because it is tuned one-fifth lower in pitch than an oboe. Its shape is similar to that of an oboe and is often played by the third oboe player in an orchestra. Flute The flute is the instrument that serves as the soprano voice in most bands, orchestras, and woodwind groups. Most flutes are made of metal and consist chiefly of a tube with a mouthpiece near one end. Piccolo The piccolo is a type of transverse flute that is pitched an octave above the concert (or standard) flute. It has a range of nearly three octaves and reaches the highest pitches of a modern orchestra. It is usually used for special effects in orchestras but is more widely used in concert and marching bands String Instruments Violin The violin, which is probably the best known orchestral instrument, is a stringed instrument that is played with a bow. The violin is the highest pitched member of the violin family, which also includes the viola, the cello, and the double bass. Viola The viola is the second highest pitched member of the violin family. It  has four strings tuned to the notes c, g, d, and a. Music for the viola is written in the alto clef. Violas vary in size, although they are always larger and tuned lower than violins. Cello The cello, also known as violoncello, is a stringed instrument which is part of the violin family. It is played with a bow much like the violin. It is also shaped liked a violin but is much larger. The cello is about four feet long and one and a half feet across at its widest part and, therefore, this member of the violin family is played sitting down String Bass The double bass (also known as the string bass, bass viol, or contrabass) is the largest and lowest pitched string instrument of the violin family. It is usually six feet high and has four strings.

Drug Abuse Essay Example for Free

Drug Abuse Essay The first edition of the report on ‘drug abuse’ has been made by our group to give an idea of the calamitous cause of using drugs in improper way. The report is intended to serve the purpose of providing the knowledge about drug abuse and to suggest ways to help limit drug abuse. An effort has been made on our part to include certain symptoms which indicate drug abuse. Also throughout the report, repetitive use of the drug abuse’ has been made to instate into the minds of the reader the cause of using drug abuse in an illicit manner The selection of the topic ‘Drug Abuse’ has been made in order to remind us of the menace of drug abuse. We live in a world where speed is the name of the game. A world where we cannot halt even for second or someone else will zip fast us to take our place. People say that it is a beautiful world if only we take time to look around. But a world has turn into a place where humanity cannot survive, only steel can. In this fast paced, ruthless, aggressive environment, there are easy ways out. Alcohol, cigarettes, drugs, are some of the most popular substances abused by people in order to include a false sense of peace, to provide a short but powerful release from the worries and troubles of their daily lives to provide a means of escape from the harsh realities of life. This report is intended to be a reminder to such folk who have let their life be washed away by drugs. In the following report, we discuss the various aspects of drug abuse. Ranging from its impact on the younger generation to the way if affects the fields of competitive sports, we presents a comprehensive survey on the topic of drug abuse. Also discussed are the physical effects caused by excessive use of drugs. Drugs like â€Å"charas† and its derivatives â€Å"bhang† have a long history of use in Indian mythology and tradition. Popular television shows, pop culture, music’s, video represents the medium through which children are influenced today. Abuse of narcotic and psychoactive stimulants  forms the core of most popular music videos. Parties in metropolitan cities today are not concluded without the customary party drugs. Available easily on the street, at rates not affected by inflation, drugs are among the most harmful items on any individual’s shopping list. In the following report, an attempt has been made to discuss the causes cure for drug abuse. This report is intended for all audiences. Acknowledgment We would like to express our gratitude to our guide and mentor Prof.Santosh Bhagat , PCT in charge , who over the past semester has guided, corrected and provided us with necessary direction whenever the need arose. But for his invaluable guidance, illuminating discussion and constant encouragement, our report would have been a distant dream. We would also like to thank Mrs. Kalyani, for her unique way of teaching us and arousing our interest towards the finer points of communication skills and report writing. Also we would like to thank all those who co-operated with us and gave their invaluable inputs, advice and suggestion to the making of this report Summary Drug abuse is the use of illegal drugs, or the misuse of prescription or over-the-counter drugs. In the sense of consuming illicit drugs like cocaine or overdose of soft drug in the medicine like crocin. Drug abuse also includes the administration of drugs by athletes to enhance their ability in the respective sport. Drug abuse can not only endanger the physical balance of the body, but also it disturbs the stability of the society. Addiction is a chronic, often relapsing brain disease that causes compulsive drug seeking and use despite harmful consequences to the  individual who is addicted and to those around them. Drug addiction is a brain disease because the abuse of drugs leads to changes in the structure and function of the brain. Although it is true that for most people the initial decision to take drugs is voluntary, over time the changes in the brain caused by repeated drug abuse can affect a person’s self control and ability to make sound decisions, and at the same time send intense impulses to take drugs. It is because of these changes in the brain that it is so challenging for a person who is addicted to stop abusing drugs. Fortunately, there are treatments that help people to counteract addiction’s powerful disruptive effects and regain control. Research shows that combining addiction treatment medications, if available, with behavioral therapy is the best way to ensure success for most patients. Treatment approaches that are tailored to each patient’s drug abuse patterns and any co-occurring medical, psychiatric, and social problems can lead to sustained recovery and a life without drug abuse. Similar to other chronic, relapsing diseases, such as diabetes, asthma, or heart disease, drug addiction can be managed successfully. And, as with other chronic diseases, it is not uncommon for a person to relapse and begin abusing drugs again. Relapse, however, does not signal failure—rather, it indicates that treatment should be reinstated, adjusted, or that alternate treatment is needed to help the individual regain control and recover. However, the main motive of the text is to minimize drug abuse. The message maintained throughout the text is to be confident in oneself and not to resort to drugs through ones phase of glum. The key is to beat drug abuse is not vigilance. It is will power, confidence and the strength of human spirit. Introduction â€Å"I’m so happy because today I’ve found my friends; They’re in my head, I’m so ugly, but that’s okay, cause so are you; We’ve broken our mirrors, Sunday morning is every day for all I care; And I’m not scared, Light my candles; In a daze, cause I’ve found GOD† -Kurt cobain, Nirvana These lyrics made into wonderful song by the band nirvana express the feelings of a person who has just administered cocaine, a drug that capsizes the human ability to think. This person is very happy with his life. All his problems seem like daze to him. He is rid of all mortal aspects of life. Then isn’t this is a wonderful experience? Well this experience caused due to administration of certain illicit drugs is called is high. This ‘high† enables the person to reach mental level of peace and calm. However, as sir Newton said â€Å"what goes up must come down .and the higher it goes the hardest it falls.â€Å" The person who administered the drug experiences a feeling known as the crash, wherein he enters into gloomy state of depression. This state of depression doesn’t leave the person till he administers the drug back again. This in turn makes the person addicted towards the drug and thus makes the person abuse drug furthermore. When an individual begins to abuse drugs, the whole family is affected. Depending on the severity of the addiction, they may begin to steal or borrow money from the family, act strange and spend days living on the street. The only thing that is important is how they will get their next high. There may be conflicts in the family about how to treat this individual. Some may continue to support him while others adopt a tough love strategy. It is difficult to know what to do, and heartbreaking to see an  individual become a slave to a drug. The recent incidents of drunken driving causing severe facilities on Indian roads are considered by many as concrete evidence of drug abuse among minor, and also as a case of severe indifference and neglect among their parents. Now, drug abuse is turning into a menace that has engulfed the world. Let’s fight collectively against this menace. Let’s learn about drug abuse. What is really means and how one can conquer it. What is drug abuse? Drug abuse, also known as substance abuse, refers to a maladaptive pattern of use of a substance that is not considered dependent. The term drug abuse does not exclude dependency, but is otherwise used in a similar manner in non medical contexts. The terms have a huge range of definitions related to taking a psychoactive drug or performance enhancing drug for a non-therapeutic or non-medical effect. All of these definitions imply a negative judgment of the drug use in question (compare with the term responsible drug use for alternative views). Some of the drugs most often associated with this term include alcohol, amphetamines, barbiturates, benzodiazepines, cocaine, methaqualone, and opioids. Use of these drugs may lead to criminal penalty in addition to possible physical, social, and psychological harm, both strongly depending on local jurisdiction. Other definitions of drug abuse fall into four main categories: public health definitions, mass communication and vernacular usage, medical definitions, and political and criminal justice definitions. Drug addiction is when you become dependent on a drug, and it forms a central part of your life. Misusing drugs can lead to physical dependency, or psychological dependency. Physical dependency means that your body has become so used to a drug that you get physical withdrawal symptoms if you stop taking it. This means that you have to keep taking the drug to stop yourself feeling ill. Psychological dependency means that you take the drug because it has formed a large part of your life, and you take it to make yourself feel good. You may feel that you cannot stop taking the drug, even though you are  not physically dependant. Some drugs can make you both physically and psychologically dependent. As you take more of a drug, your body becomes tolerant to it so it does not have such a strong effect. This means that you need to take larger amounts to get the same effect as when you started taking it. Drug misuse is when you take illegal drugs, or when you take medicines in a way not recommended by your doctor or the manufacturer. Taking medicines in very large quantities that are dangerous to your health is also an example of drug misuse. Examples of drugs that are commonly misused include: Illegal drugs, Alcohol, Tobacco, Prescribed medicines including painkillers, sleeping tablets, and cold remedies, khat (a leaf that is chewed over several hours), and Glues, aerosols, gases and solvents. What happens to your brain when you take drugs? Drugs are chemicals that tap into the brains communication system and disrupt the way nerve cells normally send, receive, and process information. There are at least two ways that drugs are able to do this: (1) by imitating the brains natural chemical messengers, and/or (2) by over stimulating the reward circuit of the brain. Some drugs, such as marijuana and heroin, have a similar structure to chemical messengers, called neurotransmitters, which are naturally produced by the brain. Because of this similarity, these drugs are able to fool the brains receptors and activate nerve cells to send abnormal messages. Other drugs, such as cocaine or methamphetamine, can cause the nerve cells to release abnormally large amounts of natural neurotransmitters, or prevent the normal recycling of these brain chemicals, which is needed to shut off the signal between neurons. This disruption produces a greatly amplified message that ultimately disrupts normal communication patterns. Nearly all drugs, directly or indirectly, target the brains reward system by flooding the circuit with dopamine. Dopamine is a neurotransmitter present in regions of the brain that control movement, emotion, motivation, and feelings of pleasure. The over stimulation of this system, which normally responds to natural behaviors that are linked to survival (eating, spending time with loved ones, etc), produces euphoric effects in response to the drugs. This reaction sets in motion a pattern that teaches people to repeat the behavior of abusing drugs. As a person continues to abuse drugs, the brain adapts to the overwhelming surges in dopamine by producing less dopamine or by reducing the number of dopamine receptors in the reward circuit. As a result, dopamines impact on the reward circuit is lessened, reducing the abusers ability to enjoy the drugs and the things that previously brought pleasure. This decrease compels those addicted to drugs to keep abusing drugs in order to attempt to bring their dopamine function back to normal. And, they may now require larger amounts of the drug than they first did to achieve the dopamine high an effect known as tolerance. Long-term abuse causes changes in other brain chemical systems and circuits as well. Glutamate is a neurotransmitter that influences the reward circuit and the ability to learn. When the optimal concentration of glutamate is altered by drug abuse, the brain attempts to compensate, which can impair cognitive function. Drugs of abuse facilitate no conscious  (conditioned) learning, which leads the user to experience uncontrollable cravings when they see a place or person they associate with the drug experience, even when the drug itself is not available. Brain imaging studies of drug-addicted individuals show changes in areas of the brain that are critical to judgment, decision making, learning and memory, and behavior control. Together, these changes can drive an abuser to seek out and take drugs compulsively despite adverse consequences in other words, to become addicted to drugs. Why do some people become addicted, while others do not? No single factor can predict whether or not a person will become addicted to drugs. Risk for addiction is influenced by a persons biology, social environment, and age or stage of development. The more risk factors an individual has, the greater the chance that taking drugs can lead to addiction. For example: Biology: The genes that people are born with in combination with environmental influences account for about half of their addiction vulnerability. Additionally, gender, ethnicity, and the presence of other mental disorders may influence risk for drug abuse and addiction. Environment:. A persons environment includes many different influences from family and friends to socioeconomic status and quality of life in general. Factors such as peer pressure, physical and sexual abuse, stress, and parental involvement can greatly influence the course of drug abuse and addiction in a persons life. Development: Genetic and environmental factors interact with critical developmental stages in a persons life to affect addiction vulnerability, and adolescents experience a double challenge. Although taking drugs at any age can lead to addiction, the earlier that drug use begins, the more likely it is to progress to more serious abuse. And because adolescents brains are still developing in the areas that govern decision making, judgment, and self-control, they are especially prone to risk-taking  behaviors, including trying drugs of abuse. Spiritual usage of cannabis (charas) in Indian history and tradition Cannabis was used in Hindu culture as early as 1500 BCE, and its ancient use is confirmed within the Vedas (Sama Veda, Rig Veda, and Atharva Veda). There are three types of cannabis used in India. The first, Bhang, consists of the leaves and plant tops of the marijuana plant. It is usually consumed as an infusion in beverage form, and varies in strength according to how much Cannabis is used in the preparation. The second, Ganja, consisting of the leaves and the plant tops, is smoked. The third, called Charas or Hashish, consists of the resinous buds and/or extracted resin from the leaves of the marijuana plant. Typically, Bhang is the most commonly used form of cannabis in religious festivals. Connection of ganja with the worship of shiva Cannabis or ganja is associated with worship of the Hindu deity Shiva, who is popularly believed to like the hemp plant. Bhang is offered to Shiva images, especially on Shivratri festival. This practice is particularly witnessed at temples of Benares, Baidynath and Tarakeswar. Bhang is not only offered to the deity, but also consumed by Shaivite (sect of Shiva) yogis. Charas is smoked by some Shaivite devotees and cannabis itself is seen as a gift (prasad, or offering) to Shiva to aid in sadhana. Some of the wandering ascetics in India known as sadhus smoke charas out of a clay chillum. During the Hindu festival of Holi, people consume a drink called bhang which contains cannabis flowers.[33][35] According to one description, when the elixir of life was produced from the churning of the ocean by the devas and the asuras, Shiva created cannabis from his own body to purify the elixir (whence, for cannabis, the epithet angaja or body-born). Another account suggests that the cannabis plant sprang when a drop of the elixir dropped on the ground. Thus, cannabis is used by sages due to association with elixir and Shiva. Wise drinking of bhang, according to religious rites, is believed to cleanse sins, unite one with Shiva and  avoid the miseries of hell in the after-life. In contrast, foolish drinking of bhang without rites is considered a sin. Regarding Buddhism, the fifth precept is to abstain from wines, liquors and intoxicants that cause heedessness. Most interpretations of the fifth precept would therfore include all forms of cannabis amongst the intoxicants that a Buddhist should abstain from consuming. However, the Buddhist precepts are guidelines whose purpose is to encourage a moral lifestyle rather than being strict religious commandments, and some lay practitioners of Buddhism may choose to consume cannabis and other mild intoxicants occasionally. Cannabis and some other psychoactive plants are specifically prescribed in the MahÄ kÄ la Tantra for medicinal purposes. However, Tantra is an esoteric teaching of Buddhism not generally accepted by most other forms of Buddhism. Drug Abuse Effects Drug abuse effects include damage to the physical, emotional, and psychological parts of the body. In addition, they compromise the social aspects of regular family, friends and job-related relationships. Drug abuse effects involve the physical body extensively and according to the kind of drugs that are used. Drug abuse effects injure the brain in a variety of ways, including: Hallucinations Mood swings Chemical imbalances Over-stimulation of dopamine (the pleasure center) Disruption of regular sleep/wake patterns Anxiety and nervous system stimulation These injuries impede regular brain processing mechanisms. They block the pathways and make the process of decision-making harder. Drug abuse effects cause lapses in memory and exaggerate reactions to events. Effects also include failing to respond to consequences and events in the environment. When someone is preoccupied with the effects of the drug or is focused on the pleasure center of the brain, they fail to notice anything else. Drug Abuse Effects and Stress Management Coping well with stressors is based on the ability to find options to obstacles. This requires observation, patience and reasoning ability. All of these coping mechanisms are compromised due to drug abuse effects. Specifically, they make stress management difficult because they: Encourage a lack of impulse control Alter the perception of events Block the ability to make sound judgments Promote oblivion; the tendency to focus on the sensations of the high to the extreme Trigger knee-jerk reactions Stimulate frustration and anger responses Other drug abuse effects encompass an array of unexpected and serious symptoms: Users of cocaine and crack experience a crash in mood elevation after the effects of the drug wear off. The crash is described as feelings of depression, craving for more of the drug, emptiness and irritability. These drug abuse effects are the prerequisite conditions to the addiction. More of  the drug is used to get rid of the negative feelings produced by the crash. Some drug abuse effects spur flashbacks. These episodes are spontaneous recurring instances similar to the high produced by the drug except that they occur at a time when the drug was not in use. Most drug abuse effects are the symptoms of withdrawal. These include poor physical coordination, nausea, anxiety, paranoia, muscle spasms and abdominal cramping. More severe drug abuse effects can be caused by the transfer of HIV and sexually transmitted diseases from person to person by sharing needles and syringes. The most devastating drug abuse effects are overdoses. Overdoses occur when people do not know how much of the drug the body can accept at one time and when increased amounts of the drug are injected or ingested in order to produce the same intensity of drug abuse effects. Effects of Drug Addiction It is often difficult with drug and alcohol addiction to decide is something a CAUSE or an EFFECT. Did the depression CAUSE the addiction or did the alcoholism cause the depression? Did the alcoholism CAUSE the family problems or where the family problems an EFFECT of the addiction. Often no one knows for sure. Q: What are the major effects of drug addiction? It is everybody’s problem. An addict might say: â€Å"I’m not hurting anybody. I’m only hurting myself.† However, we can quickly see that the statement is false, because there is no such thing as an addict who is only hurting him/herself. The problem is found everywhere, from the rich and privileged, to the lost members of society. For over 30 years the United States government has had its â€Å"War on Drugs,† but in that time frame we have seen in increase in crime, increase in health care costs and an alarming increase in the use of dangerous drugs such as cocaine, heroin, crack and methamphetamine. The â€Å"War on Drugs† has also brought on new research, a greater number of treatment facilities, new and sometimes controversial theories on treatment, advances in drug addiction medications, but are we winning? The effects of drug addiction are far reaching and can be seen in the home, on the job, in churches and in schools. Q:What are the effects of drug addiction on health? If left unchecked, the drug is going to win. Drug abuse is a disease of the brain, and the drugs change brain chemistry, which results in a change in behavior. Aside from the obvious behavioral consequences of addiction, the negative effects on a person’s health are potentially devastating. While addicts use drugs to â€Å"feel better,† the unintended consequences include but are not limited to overdose, HIV/AIDS, stroke, cardiovascular disease and a host of related maladies. To understand this better you may want to read Get Sick to Feel Better a story of the negative effects of drug addiction Darcis story of the effects of drug addiction on her life! Suicide is also a common effect of drug addiction. Depression is also an effect of addiction. Q:What are the effects of drug addiction on the family? One of the saddest aspects of the insidious nature of drug addiction is that by the time an addict realizes he/she has a problem, that problem has already taken a heavy toll on the family. Parents in treatment centers tell counselors and therapists that they want to â€Å"get their kids back,† as drug addiction has taken over to the point where the courts have been forced to remove the children from the home. Husbands and wives, brothers and sisters, and sadly children are all impacted. Families can be sources of strength and support, or they can passively enable the addiction to advance. Families can share in the victory over drug addiction, or they can be the victims of it. Q:What are the effects of drug addiction on the economy? Beyond the personal health issues, beyond the devastating effect on families, beyond community crime statistics, drug addiction has a major impact on the American economy. The National Institute on Drug Abuse reported that some $67 billion per year is the impact that drug addiction has on this country. This total includes the cost of law enforcement, incarceration, treatments, traffic injuries, lost time in the workplace, etc. Drug addiction causes impaired reasoning, and therefore the crime rate is dramatically impacted by drug use. Addicts have a much higher likelihood of committing crimes than others. Q: What are the effects of drug addiction on our society? The National Library of Medicine estimates that some 20% of all people in the  United States have used prescription medication for non-medical purposes. We’re not talking about cocaine, heroin or methamphetamine use, but doctor-prescribed medication. You can easily see that if you group the two together, illegal drug use and prescription drug misuse, we have a huge problem. Q: What are the effects of drug addiction on the Law? The news media reports daily struggles with theft, drive-by shootings, drug busts, illegal trafficking and manufacturing of drugs, and arrests for crimes ranging from child neglect to murder. Look closer and chances are great that you will uncover a drug addiction component to any of these stories. Drug Use Drug Use in the General Population According to the Substance Abuse and Mental Health Services Administration (SAMHSA) 2001 National Household Survey on Drug Abuse, 15.9 million Americans ages 12 and older (7.1%) reported using an illicit drug in the month before the survey was conducted. More than 12% reported illicit drug use during the past year and 41.7% reported some use of an illicit drug at least once during their lifetimes.