基于隐马尔可夫模型的入侵检测系统

首先介绍了基于隐马尔可夫模型(HMM)的入侵检测系统(IDS)框架,然后建立了一个计算机系统运行状况的隐马尔可夫模型,最后通过实验论述了该系统的工作过程。通过仅仅考虑基于攻击域知识的特权流事件来缩短建模时间并提高性能,从而使系统更加高效。实验表明,用这种方法建模的系统在不影响检测率的情况下,比传统的用所有数据建模大大地节省了模型训练的时间,降低了误报率。因此,适合用于在计算机系统上进行实时检测。

An Intrusion Detection System Based on Hidden Markov Model

The paper presents the framework of the Intrusion Detection System(IDS) based on hidden Markov model(HMM).Then,a hidden Markov model for the normal states of computer system is proposed.Finally,the work procedure of the proposed system is described by experiment.It proposes an effective IDS that improves the modeling time and performance with only considering the events of privilege flows based on the domain knowledge of attacks.Experimental results show that the proposed method requires significantly shorter time to train HMM without loss of detection rate and significantly reduces the false alarm rate than the other modeling method using all audit data.This method is not only useful in theory,but also can be used in practice to monitor the computer system in real time.