ARTIFICIAL INTELLIGENCE RESEARCH LABORATORY
    Center for Computational Intelligence, Learning, and Discovery
    Department of Computer Science


Security Informatics


Ensuring the security of networked computing and information infrastructure poses several research challenges that cut across different areas of computer science including distributed computing, formal specifications, software engineering, and artificial intelligence. Research in Honavar's laboratory on information assurance and computer security is focused on specification, design, and agent-based distributed implementation of adaptive systems for coordianated intrusion detection and counter-measures. This research has been supported in part by a grant from the Department of Defense.

References

  1. Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., Wang, Y., Wang, X., and Stakhanova, N. (2006) Software Fault Tree and Colored Petri Net Based Specification, Design, and Implementation of Agent-Based Intrusion Detection Systems. International Journal of Information and Computer Security. Vol. 1. No. 1. pp. In press.

  2. Wang, Y., Behera, S., Wong, J., Helmer, G., Honavar, V., Miller, L., and Lutz, R. Towards Automatic Generation of Mobile Agents for Distributed Intrusion Detection Systems. Journal of Systems and Software. Vol. 79. pp. 1-14, 2006.

  3. Kang, D-K., Silvescu, A. and Honavar, V. (2006). RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification. In: Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). Lecture Notes in Computer Science.. Berlin: Springer-Verlag.

  4. Kang, D-K., Fuller, D., and Honavar, V. (2005). Learning Misuse and Anomaly Detectors from System Call Frequency Vector Representation. In: IEEE International Conference on Intelligence and Security Informatics. Springer-Verlag Lecture Notes in Computer Science. Vol. 3495. pp. 511-516. Springer-Verlag.

  5. Kang, D-K., Zhang, J., Silvescu, A., and Honavar, V. (2005). Multinomial Event Model Based Abstraction for Sequence and Text Classification. In: Proceedings of the Symposium on Abstraction, Reformulation, and Approximation (SARA 2005). Edinburgh, UK. Vol. 3607. pp. 134-148. Berlin: Springer-Verlag.

  6. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2003). Lightweight Agents for Intrusion Detection. Journal of Systems and Software. Vol. 67. pp. 109-122.

  7. Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., and Lutz, R. (2002) A Software Fault Tree Approach to Requirements Specification of an Intrusion Detection System. Requirements Engineering. Vol 7 (4) (2002) pp. 207-220.

  8. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2002). Automated Discovery of Concise Predictive Rules for Intrusion Detection. Journal of Systems and Software.60 (3) (2002) pp. 165-175

  9. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2002). Lightweight Agents for Intrusion Detection. Journal of Systems and Software. In press.

  10. Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L. and Lutz, R. (2001). A Software Fault Tree Approach to Requirements Analysis of an Intrusion Detection System. In: Proceedings of the Symposium on Requirements Engineering for Information Security, Indianapolis, IN, USA.

  11. Helmer, J., Wong, J., Honavar, V., and Miller, L. (1998) Intelligent Agents for Intrusion Detection and Countermeasures. In: Proceedings of the IEEE Information Technology Conference. pp. 121-124.