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Day today life internet threat has been increased significantly. There is a need to develop model in order to maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The purpose of intrusion system through the security devices detect and deal with it. In this paper, a mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing infrequent data in the database. Based on defined state the degree of incoming data is evaluated through the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of attack in stages.

Evaluation of Network Intrusion Detection Using Markov Chain