Intrusion Detection Method Based on Classify Support Vector Machine

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.