Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection

International Journal of Intelligent Systems

Volumn 15, Issue 8, 2000, Pages 687-703

  Luo, Jianxiong ^a   Bridges, Susan M ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATA PRIVACY; FUZZY SETS; LEARNING ALGORITHMS; SECURITY OF DATA;

INTRUSION DETECTION; MINING FUZZY ASSOCIATION RULES;

KNOWLEDGE ACQUISITION;

EID: 0034247240     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/1098-111X(200008)15:8<687::AID-INT1>3.0.CO;2-X     Document Type: Article

References (16)

1. Lee W, Stolfo S, Mok K. Mining audit data to build intrusion detection models, In: Agrawal R, Stolorz P, editor. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, New York, August 27-31, 1998, New York: AAAI Press; 1998. p 66-72.
2. Sundaram A. An introduction to intrusion detection. (Downloaded from http://www.cs.purdue.edu/coast/archive/data/categ24.html on March 10, 1999.)
3. Frank J. Artificial intelligence and intrusion detection: current and future directions. In: Proceedings of the 17th National Computer Security Conference, October, 1994.
4. Lunt T, Jagannathan R. A prototype real-time intrusion-detection expert system. In: Proceedings of 1988 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, April 18-21, 1988. Los Alamitos, CA: IEEE Computer Society Press; 1988. p 59-66.
5. Teng H, Chen K, Lu S. Adaptive real-time anomaly detection using inductively generated sequential patterns. Proceedings of 1990 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, May 7-9, 1990. Los Alamitos, CA: IEEE Computer Society Press; 1990. p 278-284.
6. Debar H, Becker M, Siboni D. A neural network component for an intrusion detection system. In Proceedings of 1992 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, May 4-6, 1992. Los Alamitos, CA: IEEE Computer Society Press; 1992. p 240-250.
7. Ilgun K, Kemmerer A. State transition analysis: a rule-based intrusion detection approach. IEEE Trans Software Eng 1995;21(3):181-199.
8. Lunt T. 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 February 3, 1999.)
9. Agrawal R, Srikant R. Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, September 12-15, 1994. San Francisco: Morgan Kaufmann; 1994. p 487-499.
10. Mannila H, Toivonen H. Discovering generalized episodes using minimal occurrences. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, August, 1996. p 146-151.
11. Lee W, Stolfo S. Data mining approaches for intrusion detection. Proceedings of the 7th USENIX security symposium, 1998. (Downloaded from http://www.es.columbia.edu/~sal/recent-papers.html on March 10, 1999.)
12. Srikant R, Agrawal R. Mining quantitative association rules in large relational tables. Proceedings of ACM SIGMOD International Conference on Management of Data, June 4-6, 1996. p 1-12.
13. Kuok C, Fu A, Wong M. Mining fuzzy association rules in databases. SIGMOD Rec 1998;27(1):41-46.
14. Porras P, Valdes A. Live traffic analysis of TCP/IP gateways. Proceedings of the 1998 ISOC Symposium on Network and Distributed Systems Security, March, 1998.
15. Lee W, Stolfo S, Mok K. A data mining framework for building intrusion detection models. 1999. (Downloaded from http://www.cs.columbia.edu/~sal/recent-paper. html on March 10, 1999.)
16. The Institute for Visualization and Perception Research, University of Massachusetts Lowell, 1998. Information Exploration Shootout. (http://iris.cs.uml/edu:8080, accessed March 1, 1999.)