基于模糊滑窗隐马尔可夫模型的入侵检测研究

Research of intrusion detection based on fuzzy window hidden Markov model

针对传统基于隐马尔可夫模型(HMM)入侵检测中普遍存在误报与漏报过高的问题,提出了一种基于模糊窗口隐马尔可夫模型(FWHMM)的入侵检测新方法。该方法通过运用状态转移依赖滑窗的设置提高了系统的检测精度,通过将状态的随机转移转变为模糊随机转移,提高了系统的鲁棒性和自适应性。实验结果表明,使用本文方法的检测效果要明显优于基于经典HMM的方法。

To improve detection accuracy, a new intrusion detection method with high efficiency was presented, which was based on Fuzzy Window Hidden Markov Model （FWHMM）. The method improves detection accuracy by setting window of dependence between states and increases the self-adjustability and becomes lustier by changing the probability into the fuzzy random variable value. Experimental results show that the proposed method improves the detection accuracy more than the traditional HMM based method.