An efficient user verification system via mouse movements

Zheng, Nan; Paloski, Aaron; Wang, Haining

doi:10.1145/2046707.2046725

Cited by 211 publications

(126 citation statements)

References 28 publications

Supporting

Mentioning

120

Contrasting

Unclassified

Order By: Relevance

“…Initially, the two most common user behaviors with the computing environment (i.e. the use of mouse and keyboard) were investigated for the purpose of continuously and nonintrusively authenticating users: mouse dynamics [28][29][30][31][32] and keystroke analysis [33][34][35]. Then, with the prevalence of mobile devices, several new behavioral biometrics were explored for the same purpose (i.e.…”

Section: 22mentioning

confidence: 99%

Insider Misuse Identification using Transparent Biometrics

Clarke

Alruban

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

View full text Add to dashboard Cite

show abstract

Section: 22mentioning

confidence: 99%

Insider Misuse Identification using Transparent Biometrics

Clarke

Alruban

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

View full text Add to dashboard Cite

show abstract

“…This tuplet gives us the basic data element based on which all the mouse movement metrics are computed (given an initial position on the screen). In this report, we consider three metrics based on those described in [32]: (M1) curvature angle, (M2) curvature distance, and (M3) movement direction. The last is computed from a single tuplet, and the former two are computed from two adjacent tuplets.…”

Section: Configurationmentioning

confidence: 99%

“…The mouse movement curvature metrics in [32] end in a mouse click by definition. We consider a much higher density of mouse movement events, including those that do not end in a button click, but at the cost that some of these movement events may not represent any real intent from the user and thus essentially provide noise to the sensor.…”

Section: Configurationmentioning

confidence: 99%

“…The feature space is potentially endless, with frequency measurements or numeric evaluations based on features across different levels of the text, including function words [28], [29], grammar [30], character n-grams [31] and more. Although stylometry has not been used for active user authentication, its application to this sort of task brings higher level inspection into the process, compared to other lower level biometrics like mouse movements or keyboard dynamics [32], [33], discussed in the following sections.…”

Section: Introductionmentioning

confidence: 99%

“…Most studies on mouse movement were either inconclusive due to small number of users [51] or required an excessively large static corpus of mouse movement data to achieve good results [1], where an FAR and FRR of 0.0246 is achieved from a testing window of 2000 mouse actions. The work in [32] drastically reduces the size of the testing window to 20 mouse clicks. We base our selection of the three mouse metrics on their work but with more emphasis on mouse movement and not the mouse button presses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active Authentication Linguistic Modalities

Fridman¹,

Stolerman²,

Acharya³

et al. 2013

View full text Add to dashboard Cite

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) DECEMBER 2013 REPORT TYPE FINAL TECHNICAL REPORT DATES COVERED (From -To ABSTRACTActive authentication is the process of continuously verifying a user based on their on-going interaction with the computer. In this report, we consider a representative collection of behavioral biometrics: low-level modalities of keystroke dynamics and mouse movement, high-level modalities of stylometry and web browsing behavior. We develop a sensor for each modality and organize the sensors as a parallel binary detection decision fusion architecture. The decisions of each sensor (legitimate/illegitimate user) are fed into a Decision Fusion Center (DFC) which applies the Chair-Varshney fusion algorithm to generate a global decision. The DFC minimizes the probability of error using the local-sensor False Rejection Rates (FAR) and False Acceptance Rates (FRR) as well as the a-priori probability that user is legitimate to form the decision rule. We test our approach on a dataset collected from 67 users, each working individually in an office environment for a period of one week. We show that the fusion algorithm achieves lower probability of error than that of the best individual sensor in the fused set, and we are able to quantify the contribution of each modality to the overall performance. We consider the temporal characteristics of intruder detection, showing results of the time it takes to detect a change in user. We measure the effect of perfect adversarial compromise of sensors as part of the fusion system. Lastly, we consider a higher level classification model of users based on their personality metrics. Active authentication is the process of continuously verifying a user based on their on-going interaction with the computer. In this report, we consider a representative collection of behavioral biometrics: low-level modalities of keystroke dynamics and mouse movement, highlevel modalities of stylometry and web browsing behavior. We develop a sensor for each modality and organize the sensors as a parallel binary detection decision fusion architecture. The decisions of each sensor (legitimate/illeg...

show abstract

A novel attack to track users based on the behavior patterns

Shi

Ling

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

Currently, people around the world daily use the Internet to access various services, such as e-mail and online shopping. However, the behavior-based tracking attacks have posed a considerable threat to users' privacy. Relying on characteristic patterns within the Internet activities, this attack can link a user's multiple sessions. In this paper, we investigate the behavior-based tracking attack and propose some countermeasures to mitigate the threat. We preprocess the raw traffic data and then extract features ranging from lower layer network packets to high-level application-related traffic. Specifically, we focus on four types of applicationlevel traffic to infer users' habits, including HTTP, IM, e-mail, and P2P. In addition, we extract the web queries entered into shopping websites and classify them to infer users' preferences. Then, we construct the preference models and propose an improved method. For evaluation, we collect traffic in the realworld environment to construct a large-scale dataset. Five hundred and nine users are selected in terms of the user's active degree. When the term frequency-inverse document frequency transformation is used, the improved method can identify an average of 93.79% instances correctly. Our extensive empirical experiments demonstrate the effectiveness and efficiency of our approaches. Finally, we discuss and evaluate several countermeasures.In the preceding texts, d 1 to d 7 represent the top seven online shopping websites. If the user has submitted queries to the ith website in a session, the value of d i is set to 1 and 0 otherwise. c 1 to c 10

show abstract

An efficient user verification system via mouse movements

Cited by 211 publications

References 28 publications

Insider Misuse Identification using Transparent Biometrics

Insider Misuse Identification using Transparent Biometrics

Active Authentication Linguistic Modalities

A novel attack to track users based on the behavior patterns

Contact Info

Product

Resources

About