摘要: 针对入侵检测系统存在高漏报率的问题,提出一种基于猴群算法的入侵检测技术。利用猴群算法从网络审计数据KDD99数据集中生成一个分类的规则集合,采用支持度-置信度模型实现猴群算法的目标函数,以控制生成规则的质量,将动态生成的规则应用于基于规则的的入侵检测系统中。实验结果表明,基于猴群算法的入侵检测技术可改进生成规则的质量,提高入侵检测系统的检测率。
关键词:
入侵检测,
猴群算法,
支持度-置信度,
分类规则
Abstract: For the current Intrusion Detection System(IDS) has high false negative rate, this paper presents an intrusion detection technology based on Monkey Algorithm(MA). It uses the MA to derive a set of classification rules from network data, KDD99 data set, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are used to detect or classify network intrusions in a real-time environment. Experimental results show that the MA-based technology can improve the quality of generating rules, so that it can improve the performance of IDS.
Key words:
intrusion detection,
Monkey Algorithm(MA),
support-confidence,
classification rule
中图分类号:
张佳佳, 张亚平, 孙济洲. 基于猴群算法的入侵检测技术[J]. 计算机工程, 2011, 37(14): 131-133.
ZHANG Jia-Jia, ZHANG E-Beng, SUN Ji-Zhou. Intrusion Detection Technology Based on Monkey Algorithm[J]. Computer Engineering, 2011, 37(14): 131-133.