The insider threat has long been a prime security concern for government and industry organizations, and is considered one of the most serious threats in computer security. It is also considered the most difficult problem to deal with because insiders often have information and capabilities not known to external attackers, and consequently can cause serious harm, and is extremely difficult to combat, expensive, error prone, and time-consuming. It is recognized that solutions to insider threat are mainly user centric and several psychological and psychosocial models have been proposed. With the growing interest in psychological aspects of cyber security, researchers are concerned with identifying predictors of these behaviors. In this project, we investigate a comprehensive multi-dimensional study of neural activities and neurocognitive processing to develop automatic neurophysiological and computer based behavioral markers of malicious activities.
- Hassan Takabi, Yassir Hashem, and Ram Dantu. Prediction of human error using eye movements patterns for unintentional insider threat detection. In IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018, Singapore, January 11-12, 2018, pages 1–8. Pdf Bibtex
- Yassir Hashem, Hassan Takabi, and Ram Dantu. (2017). Insider Threat Detection Based on Users' Mouse Movements and Keystrokes Behavior. Conference Secure Knowledge Management Conference 2017 (SKM 2017). Pdf Bibtex
- Yassir Hashem, Hassan Takabi, Ram Dantu, and Rodey Nielsen. (2017). A multi-modal neuro-physiological study of malicious insider threats. In MIST 2017 - Proceedings of the 2017 International Workshop on Managing Insider Security Threats, co-located with CCS 2017 (Vol. 2017–Janua). http://doi.org/10.1145/3139923.3139930 Pdf Bibtex
- Parisa Kaghazgaran, and Hassan Takabi. (2017). Privacy-preserving Edit Distance on Genomic Data. arXiv preprint arXiv:1711.06234. Pdf Bibtex
- Yassir Hashem, Hassan Takabi, Mohammad Ghasemigol, and Ram Dantu. (2016). Inside the Mind of the Insider: Towards Insider Threat Detection Using Psychophysiological Signals *. Journal of Internet Services and Information Security, 6(1), 20–36. http://doi.org/10.1145/2808783.2808792 Pdf Bibtex
- Parisa Kaghazgaran, and Hassan Takabi. (2015). Toward an Insider Threat Detection Framework Using Honey Permissions. Journal of Internet Services and Information Security (JISIS), 5(3), 19–36. Pdf Bibtex
- Parisa Kaghazgaran, and Hassan Takabi. (2015). Differentially Private Decision Tree Learning from Distributed Data. 2015. ConferenceProceedings of the 2015 IEEE Symposium on Security & Privacy. Pdf Bibtex