Adaptive intrusion detection algorithm based on rough sets

Qinghua Daxue Xuebao/Journal of Tsinghua University

Volumn 48, Issue 7, 2008, Pages 1165-1168

  Zhao, Xibin ^a,b   Jing, Ranzhe ^a,b   Gu, Ming ^a  

^a TSINGHUA UNIVERSITY   (China)

^b Jiangsu Research and Development Centre for Modern Enterprise Information Software Engineering   (China)

Author keywords

Attribute reduction; Computer network security; Detection rules; Intrusion detection; Rough sets

Indexed keywords

ADAPTIVE ALGORITHMS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BACKPROPAGATION ALGORITHMS; BOOLEAN FUNCTIONS; COMPUTER CRIME; COMPUTER NETWORKS; FUZZY SETS; NEURAL NETWORKS; REAL TIME SYSTEMS; ROUGH SET THEORY; SECURITY OF DATA; SENSORS; SET THEORY; SIGNAL DETECTION;

ATTRIBUTE REDUCTION; AUTOMATIC SYSTEMS; BP NEURAL NETWORKS (BPNN); DATA DISCRETIZATION; DATA PRECONDITIONING; DETECTION RULES; INTRUSION DATA; INTRUSION DETECTION ALGORITHMS; INTRUSION DETECTION SYSTEMS (IDSS); INTRUSION DETECTION TECHNOLOGIES; NETWORK SYSTEMS; REAL-TIME PERFORMANCE; ROUGH SET ALGORITHM; ROUGH SETS; SECURITY DETECTION; SECURITY DETECTION SYSTEMS; TEST RESULTS; VECTOR MACHINES;

INTRUSION DETECTION;

EID: 48049124954     PISSN: 10000054     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

1. Bace R. Intrusion Detection [M]. New York: Macmillan Technical Publishing, 2000.
2. JIANG Jianchun, MA Hengtai, REN Dangen, et al. A survey of intrusion detection research on network security [J]. Journal of Software, 2000, 11(11): 1460-1466. (in Chinese)
3. CAI Zhongmin, GUAN Xiaohong, SHAO Ping, et al. A new approach to intrusion detection based on rough set theory [J]. Chinese Journal of Computers, 2003, 26(3): 361-366. (in Chinese)
4. Forrest S, Perrelason A S, Allen L. Self-nonself discrimination in a computer [C]//Rushby J, Meadows C. Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Oakland CA: IEEE Computer Society Press, 1994: 202-212.
5. RAO Xian, DONG Chunxi, YANG Shaoquan. An intrusion detection system based on support vector machine [J]. Journal of Software, 2003, 14(4): 789-803. (in Chinese)
6. Ghosh A K, Michael C, Schatz M. A real-time intrusion system based on learning program behavior [C]//Debar H, Wu S F (eds). Recent Advances in Intrusion Detection (RAID 2000). Toulouse: Spinger-Verlag, 2000: 93-109.
7. YANG Wu, YUN Xiaochun, LI Jianhua. An efficient approach to intrusion detection based on boosting rule learning [J]. Journal of Computer Research and Development, 2006, 43(7): 1252-1259. (in Chinese)
8. Lee W, Stolfo S J. A data mining framework for building intrusion detection model [C]//Proceedings of the 1999 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999: 120-132.
9. Pawalk Z. Rough sets [J]. Int J Computer and Information Sci, 1982, 11(5): 341-356.
10. Ziako W. Rough sets: Trends, challenges, and prospects [C]//Ziako W, Yao Y (eds). Rough Sets and Current Trends in Computing (RSCTC 2000). Banff: Springer-Verlag, 2001: 17.