Intrusion detection Based on Fuzzy support vector machines

Proceedings - International Conference on Networks Security, Wireless Communications and Trusted Computing, NSWCTC 2009

Volumn 2, Issue , 2009, Pages 639-642

  Du, Hongle ^a   Teng, Shaohua ^a   Zhu, Qingfang ^b  

^a GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^b LUOYANG NORMAL UNIVERSITY   (China)

Author keywords

Fuzzy membership function; Intrusion detection; Membership functions; Support Vector Machine

Indexed keywords

CLASSIFICATION ERROR RATE; CLASSIFICATION RESULTS; DECISION SURFACES; DETECTION ABILITY; DIFFERENT EFFECTS; FUZZY MEMBERSHIP FUNCTION; FUZZY SUPPORT VECTOR MACHINES; INPUT DATA; INPUT SAMPLE; INTRUSION DETECTION SYSTEMS; NETWORK CONNECTIVITY; NOISE DATA; OPTIMAL CLASSIFICATION; OPTIMAL SEPARATING HYPERPLANE;

COMPUTER CRIME; COMPUTER SCIENCE; DECISION MAKING; GEARS; GEOMETRY; IMAGE RETRIEVAL; INTRUSION DETECTION; MICROCONTROLLERS; MULTILAYER NEURAL NETWORKS; SUPPORT VECTOR MACHINES; WIRELESS NETWORKS;

MEMBERSHIP FUNCTIONS;

EID: 67650422468     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NSWCTC.2009.276     Document Type: Conference Paper

References (18)

1. Vapnik V. The Nature of Statistical Learning Theory [M]. New York: Springer-Verlag, 1995
2. Burges C.A A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge discovery, 1998, 2(2):121-167.
3. Inoue, T. Abe, S. Fuzzy Support Vector Machines for Pattern Classification. Proceedings of International Joint Conference on Neural Networks, Vol. 2 1449-1454(2001)
4. Lin, C.F. Wang, S.D. Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks 2002, 13(2):464-471
5. Huang, H.P.Liu, Y.H. Fuzzy Support Vector Machines for Pattern Recognition and Data Mining. International Journal of Fuzzy Systems, 2002 4(3):826-835
6. Zhang Xiang, Xiao Xiao Ling, Xu Guang You. Fuzzy Support Machine Based on Affinity among Samples. Chinese Journal of Software.2006, 17(5):951-958
7. Yanyou Hao, Zhongxian Chi, Deqin Yan, Xun Yu. An Improved Fuzzy Support Vector Machine for Credit Rating. IFIP International Federation for Information Processing 2007. 2007 495-505
8. chiang JH,Hao PY.A new kernel-based fuzzy clustering approach: Support vector clustering with cell growing.IEEE Trans.on fuzzy systems,2003,11(4):518- 527
9. Lin, C.F. Wang, S.D. Training algorithms for fuzzy support vector machines with noisy data. In: IEEE XIII Workshop on Neural Networks for Signal Processing, pp. 517-526 (2003)
10. Y.Q Wang, S.Y Lai, K.K. A New Fuzzy Support Vector Machine to Evaluate Credit Risk. IEEE Trans. On Fuzzy Systems. 2005, 13(6)
11. Rao Xian, Dong Chun-Xi, Yang Shao Quan. An Intrusion Detection System Based on Support Vector Machines. Chinese Journal of Software.2003, 14(4):798-803
12. K.L LI, H.K Wang, S.F Tian, Z.P Liu, IU Zhi Qang. Fuzzy Multi-Class Support Vector Machine and Application in Intrusion Detection. Chinese Journal of Computers.2005, 28(2):274-280
13. Weston J., Watkins C. Multi-class support vector machines. Department of Computer Science, Royal Holloway University of London Technical Report,SD-TR-98-04,1998
14. Bernhard Scholkopf, Smola A., Williamson R.C., ET a1. New support vector algorithms, Neural Computation.2000, 12(5):1207-1245
15. Forrest S. Perrelason AS. Allen L. Cherukur R.Self-Nonself discrimination in a computer[C]. Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1994.202∼12.
16. Ghosh AK, Michael C, Schatz M.Areal-time intrusion detection system based on learning program behavior[C]. Recent Advances in Intrusion Detection (RAID 2000).Toulouse: Spinger-Verlag, 2000, 93∼109.
17. Lee W, Stolfo SJ, 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.
18. Chih-Chaung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001.Software available at http://www.csie.ntu.tw/∼cjlin/libsvm