William Hill Professor in Cyber Security
Director, Center for Information Assurance
This research explores a new paradigm in user authentication in accessing in order to improve the security of computer systems.
The Adaptive Cyber-security Training (ACT) project is in collaboration with Vanderbilt University and Sparta Corporation and is involved in the development and delivery of a multi-level, multi-track cyber security training curriculum
This research explores the applicability of game theoretic approaches to address the network security issues. Thus the goal of the research is to design a solution for malicious network attacks using game theory. This ONR grant was started with Dr. Sajjan Shiva as the PI and Dr. Dipankar Dasgupta and Dr. Qishi Wu as co-PIs.
The goal of this project was to develop an intelligent multi-agent system for intrusion/anomaly detection and response in networked computers. The approach was inspired by the defense mechanisms of the immune system that is a highly distributed in nature. In this approach, immunity-based agents roam around the machines (nodes or routers), and monitor the situation in the network (i.e. look for changes such as malfunctions, faults, abnormalities, misuse, deviations, intrusions, etc.). These agents can mutually recognize each other's activities and can take appropriate actions according to the underlying security policies. Specifically, their activities are coordinated in a hierarchical fashion while sensing, communicating and generating responses. Moreover, such an agent can learn and adapt to its environment dynamically and can detect both known and unknown intrusions.
We investigated an immunity-based anomaly detection algorithm for monitoring significant changes in time series data and for data quality analysis. This anomaly detection algorithm incorporates probabilistic methods motivated by the negative selection mechanism of the immune system to detect deviations from historical behavior patterns. In particular, the detection system learns the knowledge of the domain from historical data set to generate probabilistically a set of pattern detectors that can detect any abnormalities in the behavior pattern of the monitored data series. Moreover, the detection system is distributed and dynamic (can be updated by generating a new set of detectors as the behavior shifts due to changes in organizational or operational environments). The adaptive nature of this algorithm makes it superior to the static approaches that in current practice.
The PI conducted a preliminary investigation of immunity-based computational techniques to pave the way for more complex studies of this subject in the future. The ultimate goal of this research was to develop computational techniques inspired by the natural immune system for solving real-world science and engineering problems. The natural immune system is a distributed novel-pattern recognizer, which uses intelligent mechanisms to detect a wide variety of antigens (novel patterns). From the computational point of view the immune system uses learning, memory, and associative retrieval to solve recognition and classification tasks. The immune system is a subject of great research interest, not only in the hope of finding cures for many diseases but also as a means for understanding its powerful information processing capabilities. In the current project the PI will investigate immunological principles, explore the underlying concepts and mechanisms, and take initial steps towards the development of intelligent computational techniques for solving problems in the field of science and engineering.
This instrumentation grant was used to set up a (UNIX-based) computational lab to support a number of research projects of four co-principal investigators. The intent was to acquire the equipment that will comprise a laboratory dedicated to specific research in networking, systems, software, and database. All these research projects use the equipment in various ways and share of the laboratory. These help to reduce overall costs and enhance the quality of research through improved opportunities for collaboration.