Appraise the recitation of intrusion detection system at training time

Network Intrusion Detection aims at distinguishing the behavior of the network. It is an inseparable part of the information security system. Due to rapid development of attack pattern it is necessary to develop a system which can upgrade itself as new threats are detected. Also detection rate should be high because the rate with which attack is carried out on the network is very high. In response to this problem AdaBoost Based Algorithm is proposed which has high detection rate as well as low false alarm rate. In this algorithm decision stumps are used 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. Strategies for avoiding the over fitting are adopted to improve the performance of the algorithm.