计算机工程与应用Issue(24)：74-77,4.DOI:10.3778/j.issn.1002-8331.1303-0046
基于AFSA-SVM的网络入侵检测模型
Network intrusion detection model based on improved Artificial Fish Swarm Algorithm and Support Vector Machine
李玉霞 1刘丽 2沈桂兰1
作者信息
- 1. 北京联合大学 商务学院，北京 100025
- 2. 北京联合大学 生物化学工程学院，北京 100023
- 折叠
摘要
Abstract
Feature selection is a core problem for network intrusion detection, in order to improve the detection rate of network intrusion, a network intrusion detection model(AFSA-SVM)is proposed based on Artificial Fish Swarm Algorithm and Support Vector Machine. The feature subset is coded as the position of adult fish, and the detection rate of 5 cross validation for SVM training model is taken as evaluation criteria of the feature subset, and then the fish feeding, clustering and rear-end behavior are imitated to find the optimal feature subset. The intrusion detection model is built based on the optimal feature subset. The simula-tion experiment is carried out on the KDD CUP 99 data. The results show that, compared with the Particle Swarm Optimization algorithm, Genetic Algorithm and all features, the proposed algorithm has improved detection efficiency and the detection rate of the network intrusion, so it is an efficient intrusion detection model.关键词
特征选择/人工鱼群算法/支持向量机/网络入侵检测Key words
feature selection/Artificial Fish Swarm Algorithm(AFSA)/Support Vector Machine(SVM)/intrusion detection分类
信息技术与安全科学引用本文复制引用
李玉霞,刘丽,沈桂兰..基于AFSA-SVM的网络入侵检测模型[J].计算机工程与应用,2013,(24):74-77,4.
