AI helps you reading Science
AI generates interpretation videos
AI extracts and analyses the key points of the paper to generate videos automatically
AI parses the academic lineage of this thesis
AI extracts a summary of this paper
Intrusion detection approaches are commonly divided into two categories: misuse detection and anomaly detection
A Fuzzy Data Mining Based Intrusion Detection Model
FTDCS, pp.191-197, (2004)
EI WOS SCOPUS
Mass user adoption is often a good indication of atechnology's success. We argue that the vision laid out bysome distributed computing system (DCS) pioneer that"almost everyone who uses a pencil will use a computer"is still unrealized. We review the ...
PPT (Upload PPT)
- The ubiquitous use of computers and computer networks in today’s society has made computer network security an international priority.
- Misuse detection systems are vulnerable to intruders who use new patterns of behavior or who mask their illegal behavior to deceive the detection system.
- With the anomaly detection approach, one represents patterns of normal behavior, with the assumption that an intrusion can be identified based on some deviation from this normal behavior.
- When such a deviation is observed, an intrusion alarm is produced.
- The ubiquitous use of computers and computer networks in today’s society has made computer network security an international priority
- Intrusion detection approaches are commonly divided into two categories: misuse detection and anomaly detection 
- Misuse detection systems are vulnerable to intruders who use new patterns of behavior or who mask their illegal behavior to deceive the detection system
- With the anomaly detection approach, one represents patterns of normal behavior, with the assumption that an intrusion can be identified based on some deviation from this normal behavior
- This paper describes a prototype intelligent intrusion detection system (IIDS) that is being developed to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic
- We have found that genetic algorithms can be successfully used to tune the membership functions of the fuzzy sets used by our intrusion detection system 
- The authors have integrated data mining techniques with fuzzy logic to provide new techniques for intrusion detection.
- The authors' system architecture allows them to support both anomaly detection and misuse detection components at both the individual workstation level and at the network level.
- Both fuzzy and non-fuzzy rules are supported within the system.
- The authors plan to extend this system to operate in a high performance cluster computing environment
- Sundaram, A. 1996. An introduction to intrusion detection. http://www.cs.purdue.edu/cost/archive/data/categ24.html (Accessed 10 November 1999).
- Lunt, T. 1993. Detecting intruders in computer systems. In Proceedings of 1993 conference on auditing and computer technology. (Downloaded from http://www2.csl.sri.com/nides/index5.html on 3 February 1999.)
- Teng, H., K. Chen, and S. Lu. 1990. Adaptive real-time anomaly detection using inductively generated sequential patterns. In Proceedings of 1990 IEEE computer society symposium on research in security and privacy held in Oakland, California, May 7-9, 1990, by IEEE Computer Society, 278-84. Los Alamitos, CA: IEEE Computer Society Press.
- Debar, H., M. Becker, and D. Siboni. 1992. A neural network component for an intrusion detection system. In Proceedings of 1992 IEEE computer society symposium on research in security and privacy held in Oakland, California, May 4-6, 1992, by IEEE Computer Society, 240-50. Los Alamitos, CA: IEEE Computer Society Press.
- Lee, W., S. Stolfo, and K. Mok. 1998. Mining audit data to build intrusion detection models. In Proceedings of the fourth international conference on knowledge discovery and data mining held in New York, New York, August 27-31, 1998, edited by Rakesh Agrawal, and Paul Stolorz, 66-72. New York, NY: AAAI Press.
- Ilgun, K., and A. Kemmerer.1995. State transition analysis: A rule-based intrusion detection approach. IEEE Transaction on Software Engineering 21(3): 181-99.
- Orchard, R. 1995. FuzzyCLIPS version 6.04 user’s guide. Knowledge System Laboratory, National Research Council Canada.
- Agrawal, R., and R. Srikant. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th international conference on very large databases held in Santiago, Chile, September 12-15, 1994, 487-99. San Francisco, CA: Morgan Kaufmann. (Downloaded from http://www.almaden.ibm.com/cs/people/ragrawal/papers/vldb94_rj.ps on February 1999.)
- Kuok, C., A. Fu, and M. Wong. 1998. Mining fuzzy association rules in databases. SIGMOD Record 17(1): 41-6. (Downloaded from http://www.acm.org/sigs/sigmod/record/issues/9803 on 1 March 1999).
- Luo, J. 1999. Integrating fuzzy logic with data mining methods for intrusion detection. M.S. Thesis, Mississippi State University.
- Mannila, H., and H. Toivonen. 1996. Discovering generalized episodes using minimal occurrences. In Proceedings of the second international conference on knowledge discovery and data mining held in Portland, Oregon, August, 1996, by AAAI Press, 146-51. (Downloaded from http://www.cs.Helsinki.FI/research/fdk/datamining/pubs on 19 February 1999.)
- Porras, P., and A. Valdes. 1998. Live traffic analysis of TCP/IP gateways. In Proceedings of the 1998 ISOC symposium on network and distributed systems security held in March, 1998. (downloaded from http://www2.csl.sri.com/emerald/downloads.html on 1 March 1999.)
- Wang, W., and S. Bridges. 1999. Genetic algorithm optimization of membership functions for mining fuzzy association rules. Submitted for publication to the 7th International Conference on Fuzzy Theory and Technology (FT&T 2000)
- Shi, Fajun, Susan M. Bridges, Rayford B. Vaughn 2000. The Application of Genetic Algorithms for Feature Selection in Intrusion Detection. Submitted for publication, GECCO 2000.