NDLI: Comparing classification algorithm for mouse dynamics based user identification

Content Provider	IEEE Xplore Digital Library
Author	Chao Shen Zhongmin Cai Maxion, R.A. Xiang, G. Xiaohong Guan
Copyright Year	2012
Description	Author affiliation: Dependable Systems Lab, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA (Maxion, R.A.) \|\| SKLMS Lab and MOE KLNNIS Lab, Xi'an Jiaotong University, Xi'an, Shaanxi, China (Chao Shen; Zhongmin Cai; Xiaohong Guan) \|\| Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA (Xiang, G.)
Abstract	Mouse dynamics is the process of identifying individual users based on their mouse operating behaviors. Many classification algorithms have been proposed for checking users' identity, thus it is natural to ask how well each classifier performs and how various classifiers compare to each other (e.g., to identify promising research directions). Unfortunately, we cannot conduct a valid comparison of classifiers based on the results in the literature because of inconsistent datasets, experimental conditions and methodologies across studies. The objective of this study is to obtain a dataset, to develop an objective and repeatable evaluation procedure, and to measure the performance of a range of classifiers on an equal basis. Using data from 26 subjects conducting a fixed mouse-operation task 300 times each, we implemented and evaluated twelve classifiers from the mouse-dynamics and pattern-recognition literature for a user identification task. The four top-performing classifiers achieve false-negative identification rates between 17.31% and 23.72%. The results, along with the evaluation methodology, constitute a benchmark for comparing classifiers and measuring progress for the user identification problem.
Starting Page	61
Ending Page	66
File Size	623729
Page Count	6
File Format	PDF
ISBN	9781467313841
e-ISBN	9781467313858
e-ISBN	9781467313834
DOI	10.1109/BTAS.2012.6374551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machine classification Mice Vectors Classification algorithms Acceleration Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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