Adaptive anomaly detection with evolving connectionist systems

Journal of Network and Computer Applications

Volumn 30, Issue 1, 2007, Pages 60-80

  Liao, Yihua ^a   Vemuri, V Rao ^a   Pasos, Alejandro ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Adaptive anomaly detection; Concept drift; EFuNN; Evolving connectionist systems; Fuzzy ART; Online learning

Indexed keywords

ADAPTIVE ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; ONLINE SYSTEMS;

ADAPTIVE ANOMALY DETECTION; CONCEPT DRIFT; EFUNN; EVOLVING CONNECTIONIST SYSTEMS; FUZZY ART; ONLINE LEARNING;

COMPUTER CRIME;

EID: 33750516723     PISSN: 10848045     EISSN: 10958592     Source Type: Journal    
DOI: 10.1016/j.jnca.2005.08.005     Document Type: Article

References (30)

1. Carpenter GA, Grossberg S. editors. Pattern recognition by self-organizing neural networks. Cambridge, MA: MIT Press; 1991.
2. Carpenter G.A., Grossberg S., and Rosen D.B. Fuzzy art: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4 (1991) 759-771
3. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines, 2001. URL http://www.csie.ntu.edu.tw/cjlin/libsvm
4. Debar H, Becker M, Siboni D. A neural network component for an intrusion detection system. In: Proceedings of the 1992 IEEE symposium on security and privacy. Silver Spring, MD: IEEE Computer Society; 1992. p. 240.
5. Eskin E. Anomaly detection over noisy data using learned probability distributions. In: Proceedings 17th international conference on machine learning. San Francisco, CA: Morgan Kaufmann; 2000. p. 255-62.
6. Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S. A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. In: Barbara D, Jajodia S. editors. Applications of data mining in computer security. Dordrecht: Kluwer; 2002.
7. Eskin E, Miller M, Zhong Z-D, Yi G, Lee W-A, Stolfo S. Adaptive model generation for intrusion detection systems. In: Proceedings of the ACMCCS workshop on intrusion detection and prevention. Athens, Greece, 2000.
8. Fan W. Cost-sensitive, scalable and adaptive learning using ensemble-based methods. PhD thesis, Columbia University, February 2001.
9. Forrest S, Hofmeyr SA, Somayaji A, Longstaff TA. A sense of self for unix processes. In: Proceedings of the 1996 IEEE symposium on security and privacy. Silver Spring, MD: IEEE Computer Society; 1996. p. 120.
10. Ghosh AK, Schwartzbard A. A study in using neural networks for anomaly and misuse detection. In: Proceedings of the eighth USENIX security symposium, 1999.
11. Hossain M, Bridges SM. A framework for an adaptive intrusion detection system with data mining. In: Proceedings of the 13th annual Canadian information technology security symposium. Ottawa, Canada, 2001.
12. Jang J.-S.R., Sun C.-T., and Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence (1997), Prentice-Hall, Englewood Cliffs, NJ
13. Javitz HS, Valdes A. The nides statistical component description and justification, Technical Report, Computer Science Laboratory, SRI International, Menlo Park, CA, March 1994.
14. Kasabov N. Evolving fuzzy neural networks for supervised/unsupervised on-line knowledge-based learning. IEEE Trans Man Mach Cybernet-Part B: Cybernet 31 6 (2001) 902-918
15. Kasabov N. Evolving connectionist systems: methods and applications in bioinformatics, brain study and intelligence machines. Berlin: Springer; 2002.
16. Lane T, Brodley CE. Approaches to online learning and concept drift for user identification in computer security. In: Proceedings of the fourth international conference on knowledge discovery and data mining, 1998. p. 259-63.
17. Lane T., and Brodley C.E. Temporal sequence learning and data reduction for anomaly detection. ACM Trans Inf Systems Security 2 3 (1999) 295-331
18. Lazarevic A, Ertoz L, Kumar V, Ozgur A, Srivastava J. A comparative study of anomaly detection schemes in network intrusion detection. In: Proceedings of the third SIAM international conference on data mining. San Francisco, CA: 2003. p. 25-36.
19. Lee W., and Stolfo S.J. A framework for constructing features and models for intrusion detection systems. ACM Trans Inf Systems Security 3 4 (2000) 227-261
20. Liao Y., and Vemuri V.R. Use of k-nearest neighbor classifier for intrusion detection. Comput Security 21 5 (2002) 439-448
21. Mahoney MV, Chan PK. Learning nonstationary models of normal network traffic for detecting novel attacks. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM Press; 2002. p. 376-85.
22. Maxion RA, Townsend TN. Masquerade detection using truncated command lines. In: Proceedings of international conference on dependable systems & networks. Washington DC: 2002. p. 219-28.
23. McHugh J. Intrusion and intrusion detection. Int J Inf Security 1 1 (2001) 14-35
24. Schölkopf B., Platt J.C., Schawe-Taylor J., Smola A.J., and Williamson R.C. Estimating the support of a high-dimensional distribution. Neural Comput 13 7 (2001) 1443-1471
25. Sequeira K, Zaki MJ. Admit: anomaly-based data mining for intrusions. In: Proceedings of SIGKDD, 2002. p. 386-95.
26. Teng HS, Chen K, Lu SC. Adaptive real-time anomaly detection using inductively generated sequential patterns. In: Proceedings of IEEE symposium on security and privacy. Oakland, CA: 1990. p. 278-84.
27. The third international knowledge discovery and data mining tools competition (kdd cup 1999) data, 1999. URL http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
28. Valdes A. Detecting novel scans through pattern anomaly detection. In: Proceedings of DARPA information survivability conference and exposition. Washington, DC: 2003. p. 140-51.
29. Vapnik V. Statistical learning theory (1998), Wiley, New York
30. Yamanishi K., Takeuchi J., and Williams G. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery 8 (2004) 275-300