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Content Provider | IEEE Xplore Digital Library |
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Author | Meijuan Gao Jingwen Tian Mingping Xia |
Copyright Year | 2009 |
Abstract | Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of support vector machine (SVM), an intrusion detection method based on classify SVM is presented in this paper. The SVM network structure for intrusion detection is established, and use the genetic algorithm (GA) to optimize SVM parameters, thereby enhancing the convergence rate and the detection accuracy. We discussed and analyzed the affect factors of network intrusion behaviors. With the ability of strong self-learning and well generalization of SVM, the intrusion detection method based on classify SVM can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective. |
Starting Page | 391 |
Ending Page | 394 |
File Size | 663909 |
Page Count | 4 |
File Format | |
ISBN | 9780769538044 |
DOI | 10.1109/ICICTA.2009.330 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-10-10 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Automation Uncertainty Artificial neural networks Educational institutions intrusion detection intrusion behaviors Genetic algorithms Support vector machines genetic algorithm Intelligent networks support vector machine Intrusion detection Support vector machine classification Artificial intelligence |
Content Type | Text |
Resource Type | Article |
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