Honavar's
primary expertise is in Computational Intelligence,
and in particular, the design and applications
of distributed and security preserving data
mining algorithms, information integration
and knowledge acquisition from heterogeneous,
distributed information sources, and intelligent
agents and multi-agent systems. Honavar has
extensive experience with development of a
wide variety of data mining algorithms (neural
networks, bayesian networks, relational learning,
grammar inference) and their application to
a wide range of problems including automated
diagnosis, automated knowledge acquisition,
and data-driven intrusion modeling and detection.
Honavar's research in information assurance
has focused on design and implementation of
information infrastructures for monitoring
and detection of intrusions in distributed
networks (with emphasis on coordinated attacks).
This work has led to the development of formal
methods for modeling intrusions and countermeasures
(including attack trees and colored petri
nets), development of novel approaches for
information integration from multiple heterogeneous,
autonomous, information sources to support
monitoring and protection of distributed systems
against coordinated attacks, and data representations
and data mining algorithms for data-driven
intrusion modeling. Some of this work has
been carried out in collaboration with Johnny
Wong, Les Miller, Robyn Lutz, and Guy Helmer.
This research has been funded in part by
grants from the Department of Defense and
the National Science Foundation.
1. Caragea, D., Silvescu, A., Honavar,
V. (2001). Analysis and Synthesis of Agents
that Learn from Distributed Dynamic Data
Sources. Invited chapter. In: Wermter, S.,
Willshaw, D., and Austin, J. (Ed.). Emerging
Neural Architectures Based on Neuroscience.
Springer-Verlag. 2. 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. In press.
3. Helmer, G., Wong, J., Honavar, V., and
Miller, L. (2002). Lightweight Agents for
Intrusion Detection. Journal of Systems
and Software. In press.
4. Helmer, G., Wong, J., Honavar, V., and
Miller, L. (2002). Automated Discovery of
Concise Predictive Rules for Intrusion Detection.
Journal of Systems and Software. Vol. 60.
No. 3. pp. 165-175.
5. 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.
6. Helmer, G., Wong, J., Slagell, M., Honavar,
V., Miller, L., and Lutz, R. (2002). Software
Fault Tree and Colored Petri Net Based Specification,
Design and Implementation of Agent-Based
Intrusion Detection Systems. To appear.
7. Leiva, H., Atramentov, A., and Honavar,
V. (2002). Experiments with MRDTL -- A Multirelational
Decision Tree Learning Algorithm. In: Proceedings
of the Workshop on Multi-Relational Decision
Tree Learning. Berlin: Springer-Verlag.
In press.
8. Reinoso-Castillo, J., Silvescu, A.,
and Honavar, V. (2002). A Federated Query-Centric
Approach to Ontolgy-Driven Information Extraction
and Integration from Autonomous, Heterogeneous,
Distributed Data Sources. To appear.
9. Wong, J., Helmer, G., Naganathan, V.
Polavarapu, S., Honavar, V., and Miller,
L. (2001) SMART Mobile Agent Facility. Journal
of Systems and Software. vol. 56. pp. 9-22.
10. Zhang, J., Silvescu, A., and Honavar,
V. Ontology-Driven Induction of Decision
Trees at Multiple Levels of Abstraction.
In: Proceedings of the Symposium on Abstraction,
Reformulation, and Approximation (SARA-2002).
Kananaskis, Alberta, Canada. Lecture Notes
in Computer Science. Berlin: Springer-Verlag.
