摘要
入侵检测系统不仅要具备良好的入侵检测性能,同时对新的入侵行为要有良好的增量式学习能力.提出了一种入侵检测集成分类系统,将主成分分析(PCA)和独立成分分析(ICA)与增量式支持向量机分类算法相结合构造两个子分类器,采用集成技术对子分类器进行集成.系统利用支持向量集合对已有的入侵知识进行压缩表示,并采用遗传算法自适应地调整集成分类系统的权重.数值实验表明:集成分类系统通过自适应训练权重,综合了两种特征提取子分类器的优点,具有更好的综合性能.
Intrusion detection system should be able to detect intrusion behaviors and learn novel intrusion types. In this paper, an intrusion detection ensemble system is proposed, which is integrated by two incremental SVM （support vector machine） subsystems. The two subsystems process the features extracted by PCA and ICA respectively. The intrusion information is represented by support vectors set and the weight of the integration is adjusted by genetic algorithm. Experiments show that the ensemble system combines the advantages of the two subsystems, and outperforms each of the subsystems and the standard SVM system.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2006年第4期633-638,共6页
Journal of Computer Research and Development
基金
云南省自然科学基金项目(2005F0028Q)
云南省教育厅基金项目(5Y0588D)
