A novel hybrid intrusion detection method integrating anomaly detection with misuse detection

Expert Systems with Applications

Volumn 41, Issue 4 PART 2, 2014, Pages 1690-1700

  Kim, Gisung ^a   Lee, Seungmin ^b   Kim, Sehun ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Electronics and Telecommunications Research Institute (ETRI)   (South Korea)

Author keywords

Anomaly detection; Decision tree; Hybrid intrusion detection; One class SVM

Indexed keywords

ANOMALY DETECTION; ANOMALY DETECTION MODELS; C4.5 DECISION TREE ALGORITHM; CONVENTIONAL METHODS; DECOMPOSITION STRUCTURES; HYBRID INTRUSION DETECTION; ONE CLASS-SVM; TRAINING AND TESTING;

DATA MINING; DECISION TREES;

INTRUSION DETECTION;

EID: 84888315965     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.08.066     Document Type: Article

References (15)

1. D. Anderson, T. Frivold, and A. Valdes Next-generation intrusion detection expert system (NIDES), software users manual, beta-update release 1995 SRI International, Computer Science Laboratory
2. Barbara, D., Couto, J., Jajodia, S., Popyack, L., & Wu, N. (2001). ADAM: Detecting intrusions by data mining. In Proceedings of the IEEE workshop on information assurance and security (pp. 11-16).
3. F. Chang, C. Guo, X. Lin, and C. Lu Tree decomposition for large-scale SVM problems Journal of Machine Learning Research 10 2010 2935 2972
4. C. Chang, and C. Lin LIBSVM: A library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 3 2011 27:1 27:27 Software available at < http://www.csie.ntu.edu.tw/~cjlin/libsvm >
5. O. Depren, M. Topallar, E. Anarim, and M.K. Ciliz An intelligent intrusion detection system for anomaly and misuse detection in computer networks Expert Systems with Applications 29 4 2005 713 722 (Pubitemid 41394445)
6. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The WEKA data mining software: An update ACM SIGKDD Explorations Newsletter 11 1 2009 10 18
7. K. Hwang, Y. Chen, and M. Qin Hybrid intrusion detection with weighted signature generation over anomalous Internet episodes IEEE Transactions on Dependable and Secure Computing 4 1 2007 41 55 (Pubitemid 46384620)
8. L.M. Manevitz, and M. Yousef One-class SVMs for document classification Journal of Machine Learning Research 2 2002 139 154
9. Perdisci, R., Gu, G., & Lee, W. (2006). Using an ensemble of one-class SVM classifiers to harden payload-based anomaly detection systems. In Proceedings of the 6th international conference on data mining (pp. 488-498). (Pubitemid 47485828)
10. J. Quinlan Learning decision tree classifier ACM Computing Surveys (CSUR) 28 1 1996 71 72
11. B. Schölkopf, J.C. Platt, J. Shawe-Taylor, A.J. Smola, and R.C. Williamson Estimating the support of a high-dimensional distribution Neural Computation 13 7 2001 1443 1471 (Pubitemid 33595028)
12. H.J. Shin, D.H. Eom, and S.S. Kim One-class support vector machines: An application in machine fault detection and classification Computers & Industrial Engineering 48 2 2005 395 408 (Pubitemid 40274932)
13. J. Song, H. Takakura, Y. Okabe, and Y. Kwon Unsupervised anomaly detection based on clustering and multiple one-class SVM IEICE Transactions on Communications E92-B 6 2009 1982 1990
14. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. (2009). A detailed analysis of the KDD CUP 99 data set. In Proceeding of the IEEE symposium on computational intelligence for security and defense applications (pp. 53-58).
15. Zhang, J., & Zulkernine, M. (2006). A hybrid network intrusion detection technique using random forests. In Proceedings of the first international conference on availability, reliability and security (pp. 262-269). (Pubitemid 44732653)