Data Mining Techniques for Intrusion Detection

Intrusion detection can be defined as an act of detecting actions that attempt to compromise the confidentiality, integrity or availability of any network resource. In this paper we discuss the different data mining techniques for intrusion detection. We review some of the existing ensemble methods used in intrusion detection. We also propose an ensemble method for the problem of intrusion detection.

[1]  Fabio Roli,et al.  Intrusion detection in computer networks by multiple classifier systems , 2002, Object recognition supported by user interaction for service robots.

[2]  Aurobindo Sundaram,et al.  An introduction to intrusion detection , 1996, CROS.

[3]  Andrew H. Sung,et al.  Intrusion detection using an ensemble of intelligent paradigms , 2005, J. Netw. Comput. Appl..

[4]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[5]  Siti Mariyam Shamsuddin,et al.  Ensemble classifiers for network intrusion detection system , 2009 .

[6]  Salvatore J. Stolfo,et al.  A data mining framework for building intrusion detection models , 1999, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344).

[7]  Manas Ranjan Patra,et al.  Ensemble Voting System for Anomaly Based Network Intrusion Detection , 2009 .

[8]  Pravin Shetty DISTRIBUTED INTRUSION: Detection Systems , 2010 .

[9]  Ajith Abraham,et al.  Distributed Intrusion Detection Systems: A Computational Intelligence Approach , 2008 .

[10]  Charles Elkan,et al.  Results of the KDD'99 classifier learning , 2000, SKDD.

[11]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.

[13]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.