Intrusion Detection System using Bayesian Network and Hidden Markov Model

Abstract

Across the globe, billions of dollars are spending every year to provide security to the network systems to prevent the intrusions. Some consider the disruption of the vital systems as a serious threat which disables the work of hospitals, banks, military and various internet services across the world. To avert this impending threat, there are many possible solutions: one of these solutions is intrusion detection systems (IDS). The paper proposes to discuss the IDS model in its elaboration using Bayesian Network and the Hidden Markov Model (HMM) approach with KDDCUP dataset. The IDS framework has been designed with various levels of processing such as model learning with training data and constructing the Bayesian Network and this structure has been used as HMM state transition diagram. The preprocessed KDDCUP dataset has been used to train and test the model. The IDS model has been trained and tested for normal and attack type connection records separately. The results evince that the performance of the model is of high order for classification of normal and intrusions attacks.