PERFORMANCE COMPARISON OF ADABOOST BASED WEAK CLASSIFIERS IN NETWORK INTRUSION DETECTION

Recently machine learning based Intrusion Detection System (IDS) developments have been subjected to extensive researches because they can detect both misuse detection and anomaly detection. In this paper, we propose an AdaBoost based algorithm for network IDS with single weak classifier. In this algorithm, the classifiers such as Bayes Net, Naive Bayes and Decision tree are used as weak classifiers. KDDCup99 dataset is used in these experiments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak classification algorithms. Our approach achieves higher detection rate with low false alarm rates and is scalable for large datasets, resulting In an effective intrusion detection system.