基于数据挖掘的入侵检测系统智能结构模型

为了提高对拒绝服务攻击、内存溢出攻击、端口扫描攻击和网络非法入侵等发现的有效性以及对海量的安全审计数据能进行智能化处理,采用数据挖掘的方法从大量的信息中提取有威胁的、隐蔽的入侵行为及其模式.将数据挖掘的聚类分析方法与入侵检测系统相结合,提出了一种入侵检测系统的智能结构模型.实验表明,它能够有效地从海量的网络数据中进行聚类划分,找到相关的入侵数据,从而提高对各种攻击类型网络入侵检测的效率.

Intellectual structure pattern of intrusion detection system based on data mining

Data Mining is adopted to extract dangerous and concealed intrusive actions and patterns from a lot of information. Thus it willimprovevaliditythatcandiscoverDenialofService, attack over the spill-out ofprimary memory, attack with port scanning, illegal Internet intrusion, etc, And it will process a magnitude of safe and auditable data intelligently. Clustering analytical means of Data Mining is combined with Intrusion Detection System and an intellectual structure pattern used in Intrusion Detection System is proposed. Experiments show that clustering division can be achieved effectively from a magnitude of network data. The data on Internet intrusion can be discovered. So the efficiency of all kinds of Internet Intrusion Detections is improved.