计算机工程与应用Issue(3)：75-78,107,5.DOI:10.3778/j.issn.1002-8331.1203-0713
改进蚁群算法和支持向量机的网络入侵检测
Network intrusion detection by combination of improved ACO and SVM
肖国荣1
作者信息
- 1. 广东金融学院 计算机科学与技术系，广州 510521
- 折叠
摘要
Abstract
In order to improve network intrusion detection accuracy, this paper proposes a network detection method based on improved Ant Colony Optimization algorithm(ACO)and Support Vector Machine(ACO-SVM). The parameters of SVM model are considered as the position vector of ants. Target individuals which lead the ant colony to do global rapid search are determined by dynamic and stochastic extraction, and the optimal ant of this generation searches in small step nearly. The optimal parameter value is obtained by ACO. The network intrusion detection model is obtained. The ACO-SVM performance is tested by KDD CUP99 data. The results show that the proposed method has improved the network anomaly detection accuracy, and reduced the false alarm rate.关键词
网络入侵/支持向量机/蚁群算法/检测Key words
network intrusion/Support Vector Machine(SVM)/Ant Colony Optimization(ACO)algorithm/detection分类
信息技术与安全科学引用本文复制引用
肖国荣..改进蚁群算法和支持向量机的网络入侵检测[J].计算机工程与应用,2014,(3):75-78,107,5.
