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Start date:

October 2001


UA-CCIT and U.S. Army (invited)

Working staff:

Faculty - Dr. Olivia Sheng, UA-MIS
Students - Wanshiou Yang, National Sun Yat Sen University, Taiwan Charlie Chi, UA- Computer Science


With the tremendous growth of computers and computing devices, information system security has become an issue of serious global concern. Developing effective methods for preventing and detecting intrusions, therefore, will be essential for assuring system security. In addition to many prevention techniques, intrusion detection system is often used as another wall to protect computer systems. Building an effective intrusion detection system, however, is an enormous knowledge engineering task. Experts first analyze and categorize attack scenarios and system vulnerabilities, and hand-code the corresponding rules and patterns of detection. Because of manual and ad hoc nature of the development process, current intrusion detection systems have limited extensibility and adaptability. In this research, therefore, we take a data-centric point of view, and consider intrusion detection as a data analysis process. The central theme of our approach is to apply data mining methods to the extensively gathered audit data to discover user's unusual, deviated patterns. By integrating these patterns, our research aims to develop a more automated approach for improving the detecting ability of intrusion detection system.



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