Aim of an intrusion detection system (IDS) is to distinguish the behavior of network. IDS should upgrade itself so as to cope up with the changing pattern of attacks. Also detection rate should be high since attack rate on the network is very high. In response to this problem, Pattern Based Algorithm is proposed which has high detection rate and low false alarm rate. The work is related to the development of pattern based IDS using supervised approach. The algorithm uses decision stumps as weak classifier. The decision rules are provided for both categorical and continuous features. Weak classifier for continuous features and weak classifier for categorical features are combined to form a strong classifier. The experimentation is performed on KDD CUP 99 dataset and NSL KDD data which is revised KDD CUP 99 data.

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Pattern supervised learning Intrusion detection system AdaBoost Machine Learning