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Evolutionary Neural Networks for Anomaly Detection Based on the Behavior of a Program.
- Source :
- IEEE Transactions on Systems, Man & Cybernetics: Part B; Jun2006, Vol. 36 Issue 3, p559-571, 13p, 2 Black and White Photographs, 6 Diagrams, 8 Charts, 5 Graphs
- Publication Year :
- 2006
-
Abstract
- The process of learning the behavior of a given pro- gram by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods be- cause there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10834419
- Volume :
- 36
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics: Part B
- Publication Type :
- Academic Journal
- Accession number :
- 21098884
- Full Text :
- https://doi.org/10.1109/TSMCB.2005.860136