基于最大频繁项集挖掘的入侵检测研究

Intrusion Detection Based on Maximal Frequent Items Set Mining

通过建立基于最大频繁项集系统的正常行为模型与攻击模型;采用滑动窗口是否有不被正常行为模型覆盖的频繁模式产生来检测入侵,提高在短时间内对频繁发生的攻击类型的检测精度和响应速度.

This paper establishs the system＇ s normal profile model and attack model by mining the maximal frequent item sets, it employs a sliding window to check record in the test datasets by normal profile model to detect if any attack is taking place. The experimental results show this method is efficient and accurate for the attacks that occur intensively in a short period of time.