Multi Stage Filter Using Enhanced Adaboost for Network Intrusion Detection

Based on the analysis and distribution of network attacks in KDDCup99 dataset and real time traffic, this paper proposes a design of multi stage filter which is an efficient and effective approach in dealing with various categories of attacks in networks. The first stage of the filter is designed using Enhanced Adaboost with Decision tree algorithm to detect the frequent attacks occurs in the network and the second stage of the filter is designed using enhanced Adaboost with Nave Byes algorithm to detect the moderate attacks occurs in the network. The final stage of the filter is used to detect the infrequent attack which is designed using the enhanced Adaboost algorithm with Nave Bayes as a base learner. Performance of this design is tested with the KDDCup99 dataset and is shown to have high detection rate with low false alarm rates.