Traditional network intrusion detection algorithm is based on pattern matching, which has made great progress in network intrusion detection system, but the efficiency of this algorithm for data packet matching is quite low. With the rapid increase of Internet scale and capacity, the general information security problem appears, and it brought hidden danger for an open network security. In this paper, the author analyse the intrusion detection and performance simulation based on improved sequential pattern mining algorithm. We integrate the data mining algorithms to implement the IDS, and the simulation result reflects the effectiveness of the methodology. The simulation shows that when minimum support is very small, PrefixSpan running time running a lot less time than other algorithm, and the difference between the two is obvious. Due to the mining algorithm of the relative independence of intrusion detection system, algorithm does not depend on the specific data and specific system, so the intrusion detection system based on data mining to data source requirement is very low.

传统的网络入侵检测算法是基于模式匹配的，在网络入侵检测系统中取得了长足的进步，但是该算法对数据包的匹配效率很低。随着Internet规模和容量的快速增长，出现了普遍的信息安全问题，并为开放的网络安全带来了隐患。本文分析了基于改进的顺序模式挖掘算法的入侵检测和性能仿真。我们集成了数据挖掘算法来实现IDS，仿真结果反映了该方法的有效性。仿真表明，当最小支持量很小时，PrefixSpan的运行时间比其他算法的运行时间要少得多，两者之间的区别是显而易见的。