The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
User re-authentication via mouse movements
Abstract
We present an approach to user re-authentication based on the data collected from the computer's mouse device. Our underlying hypothesis is that one can successfully model user behavior on the basis of user-invoked mouse movements. Our implemented system raises an alarm when the current behavior of user X, deviates sufficiently from learned "normal" behavior of user X. We apply a supervised learning method to discriminate among k users. Our empirical results for eleven users show that we can differentiate these individuals based on their mouse movement behavior with a false positive rate of 0.43% and a false negative rate of 1.75%. Nevertheless, we point out that analyzing mouse movements alone is not sufficient for a stand-alone user re-authentication system.
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Cites
A comparison of methods for multiclass support vector machines
Hsu, C.-W., & Lin, C.-J. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,838
Denning, D. E. (1987) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1987.232894 · Cited by 3,427
High confidence visual recognition of persons by a test of statistical independence
Daugman, J. G. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.244676 · Cited by 3,252
Boosting a Weak Learning Algorithm by Majority
Freund, Y. (1995) · Information and Computation · doi:10.1006/inco.1995.1136 · Cited by 1,674
Intrusion detection using sequences of system calls
Hofmeyr, S. A., Forrest, S., & Somayaji, A. (1998) · Journal of Computer Security · doi:10.3233/jcs-980109 · Cited by 1,313
A brief introduction to boosting
Schapire, R. E. (1999) · Cited by 1,292
A data mining framework for building intrusion detection models
Lee, W., Stolfo, S. J., & Mok, K. W. (2003) · Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344) · doi:10.1109/secpri.1999.766909 · Cited by 1,148
Detecting intrusions using system calls: alternative data models
Warrender, C., Forrest, S., & Pearlmutter, B. (2003) · Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344) · doi:10.1109/secpri.1999.766910 · Cited by 1,106
Forrest, S., Hofmeyr, S. A., & Somayaji, A. (1997) · Communications of the ACM · doi:10.1145/262793.262811 · Cited by 848
State transition analysis: a rule-based intrusion detection approach
Ilgun, K., Kemmerer, R. A., & Porras, P. A. (1995) · IEEE Transactions on Software Engineering · doi:10.1109/32.372146 · Cited by 747
Cited by
Frank, M., Biedert, R., Ma, E., Martinovic, I., & Song, D. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2225048 · Cited by 844
A review of emotion recognition methods based on keystroke dynamics and mouse movements
Kolakowska, A. (2013) · 2013 6th International Conference on Human System Interactions (HSI) · doi:10.1109/hsi.2013.6577879 · Cited by 130
Multi modal decision fusion for continuous authentication
Fridman, L., Stolerman, A., Acharya, S., Brennan, P., Juola, P., Greenstadt, R., & Kam, M. (2015) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2014.10.018 · Cited by 64
Challenges and opportunities in applied machine learning
Brodley, C. E., Rebbapragada, U., Small, K., & Wallace, B. C. (2012) · AI Magazine · doi:10.1609/aimag.v33i1.2367 · Cited by 32
Self-citationAttacking mouse dynamics authentication using novel Wasserstein conditional DCGAN
Roy, A., Wong, K., & Phan, R. C.-W. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3245418 · Cited by 16
Metadata
- Authors
- Pusara, M., & Brodley, C. E.
- Venue
- Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
- Publisher
- ACM
- Published
- 2004-10-29
- Pages
- 1-8
- OpenAlex
- W1977470670
- Cited by
- 283
- References
- 27
- FWCI
- 4.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Pusara, M., & Brodley, C. E. (2004). User re-authentication via mouse movements. Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security. https://doi.org/10.1145/1029208.1029210
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
