Incorporating Hidden Markov Model into Anomaly Detection Technique for Network Intrusion Detection

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Now-a-days to increase the computation efficiency distributed systems are used in which the computing resources are shared among several systems. Such openness of distributed system leads to increase in potential attacks on the hardware and software by exploration of system vulnerability. This paper presents implementation of Intrusion Detection System (IDS) to model the behavior of users using Hidden Markov Model (HMM). This model attempts to detect intrusive attack efficiently. The IDS is an identification system which can be characterized by probabilities of false acceptance and false rejection. False acceptance means that the IDS allow intruders to continue their activity. False rejection means that the IDS stop the activity of a legitimate user.