数据挖掘在多Agent入侵检测系统中的应用

APPLICATION OF DATA MINING IN MULTI-AGENT INTRUSION DETECTION SYSTEM

入侵检测是保护网络系统安全的关键技术和重要手段。采用数据挖掘的方法可以从大量信息中提取入侵行为模式,将数据挖掘技术与入侵检测系统相结合,提出了一种基于数据挖掘的多Agent入侵检测系统模型和最大频繁项集挖掘算法。该算法可以自动从大量的网络数据中提取用户的行为和入侵特征模式。实验表明,该方法提高了入侵检测系统中频繁模式的挖掘效率,也提升了整个系统的性能。

Intrusion Detection is a key technology and important means to insure network systems security.Taking advantage of the data mining means can help extracting intrusion behaviour patterns from a vast amount of information.Data mining technology and intrusion detection system is integrated together for the proposition of data-mining-based multi-Agent intrusion detection system model and maximum frequent item set mining algorithm.The algorithm realises automatic extraction of user behaviour and intrusion feature pattern from a vast amount of network data.Experimental results show that the method improves the frequent mode mining efficiency in the intrusion detection system and hence improves the overall system performance.