计算机工程与应用2012，Vol.48Issue(35)：71-74,105,5.DOI:10.3778/j.issn.1002-8331.1205-0365
粒子群优化支持向量机的入侵检测算法
New network intrusion detection algorithm based on support vector machine and particle swarm optimization
刘明珍1
作者信息
- 1. 湖南涉外经济学院实验中心,长沙410205
- 折叠
摘要
Abstract
In order to improve the detection accuracy network intrusion detection, this paper proposes a novel network intrusion detection method, namely the BPSO-SVM-based detection algorithm that combines Binary Particle Swarm Optimization (BPSO) and Support Vector Machine (SVM) techniques to cope with feature selection issue for network intrusion. In the proposed algorithm, network intrusion detection is regarded as a multi-class categorization problem and feature subset is selected using a wrapper model, in which the BPSO searches the whole feature space and a SVM classifier serves as an evaluator for the goodness of the feature subset selected by the BPSO. The experimental results show that the proposed method reduces features dimensionality greatly and improves the detection accuracy of network intrusion as well as the significant improvement on detection speed.关键词
网络入侵检测/二值粒子群优化/支持向量机/特征选择Key words
network intrusion detection/ binary particle swarm optimization/ support vector machine/ feature selection分类
信息技术与安全科学引用本文复制引用
刘明珍..粒子群优化支持向量机的入侵检测算法[J].计算机工程与应用,2012,48(35):71-74,105,5.
