Incremental SVM based on reserved set for network intrusion detection

Expert Systems with Applications

Volumn 38, Issue 6, 2011, Pages 7698-7707

  Yi, Yang ^a   Wu, Jiansheng ^a   Xu, Wei ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

Incremental support vector machine; Modified kernel function; Network intrusion detection; Reserved set

Indexed keywords

ACADEMIC RESEARCH; COMPUTATION PROCESS; CONCENTRIC CIRCLES; INCREMENTAL SUPPORT VECTOR MACHINE; KERNEL FUNCTION; LEARNING PROCESS; MEAN SQUARE DIFFERENCE; MODIFIED KERNELS; NETWORK INTRUSION DETECTION; OSCILLATION PHENOMENON; OSCILLATION PROBLEMS; RESERVED SET; SUPPORT VECTOR; SVM ALGORITHM; TRAINING TIME;

RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

INTRUSION DETECTION;

EID: 79951581599     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.12.141     Document Type: Article

References (17)

1. Anderson, J. P. (1980). Computer security threat monitoring and surveillance. Technical Report, 98 (17). Fort Washington, PA, USA: James P Anderson Co.
2. K. Burbeck, and N.T. Simmin Adaptive real-time anomaly detection with incremental clustering Information Security Technical Report 12 1 2007 56 67 (Pubitemid 46829996)
3. Denning, D. E.; Neumann, P. G. (1985). Requirements and model for IDES-A real-time intrusion detection system. Technical Report, Computer Science Laboratory. Menlo Park, CA: SRI International.
4. Eskin, E. (2000). Anomaly detection over noisy data using learned probability distributions. In Proceedings of the international conference on machine learning (pp. 255-262).
5. Fei, R.; Hu, L.; Liang, H. (2008). Using density-based incremental clustering for anomaly detection. In Proceedings of the 2008 international conference on computer science and software engineering (Vol. 3, pp. 986-989).
6. Hu, W.; Liao, Y.; Vemuri, V. R. (2003). Robust support vector machines for anomaly detection in computer security. In Proceedings of the 2003 international conference on machine learning and applications (pp. 23-24).
7. KDD99, KDD cup 1999 data. (1999). .
8. Lee, J. H.; Lee, J. H.; Sohn, S. G.; Ryu, J.H.; Chung, T. M. (2008). Effective value of decision tree with KDD 99 intrusion detection datasets for intrusion detection system. In The 10th international conference on advanced communication technology (ICACT 2008) (Vol. 2, pp. 1170-1175).
9. Y. Liu, Z. Wang, Y. Feng, and H.Y. Gu Dos intrusion detection based on incremental learning with support vector machines Computer Engineering 32 4 2006 179 186
10. A. Patcha, and J.M. Park Network anomaly detection with incomplete audit data Computer Networks 51 13 2007 3935 3955 (Pubitemid 47042596)
11. S. Peddabachigari, A. Abraham, C. Grosan, and J. Thomas Modeling intrusion detection system using hybrid intelligent systems Journal of Network and Computer Applications 30 1 2007 114 132 (Pubitemid 44666486)
12. Shon, T.; Kim, Y.; Lee, C.; Moon, J. (2005). A machine learning framework for network anomaly detection using SVM and GA. In Proceedings of the 2005 IEEE workshop on information assurance and security (pp. 176-183). (Pubitemid 43948720)
13. Srinoy, S. (2007). Intrusion Detection model based on particle swarm optimization and support vector machine. In Proceedings of the 2007 IEEE symposium on computational intelligence in security and defense applications (CISDA 2007) (pp. 186-192). (Pubitemid 47431354)
14. Syed, N.; Liu, H.; Sung, K. (1999). Handing concept drifts in incremental learning with support vector machines. In Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 317-321).
15. X.D. Wang, C.Y. Zheng, C.M. Wu, and H.D. Zhang New algorithm for SVM-based incremental learning Computer Applications 26 10 2006 2440 2443
16. C. Xiang, P.C. Yong, and L.S. Meng Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees Pattern Recognition Letters 29 7 2008 918 924
17. Z. Zhang, and H. Shen Application of online-training SVMs for real-time intrusion detection with different considerations Computer Communications 28 12 2005 1428 1442 (Pubitemid 40952614)