A Novel Multi-Classifier Layered Approach to Improve Minority Attack Detection in IDS

Due to the tremendous growth of network based services, intrusion detection has emerged as an important technique for network security. While variety of security techniques are being developed and a lot of research is going on intrusion detection, but the field lacks an integrated approach with high detection rate (recall) and precision for minority attacks namely R2L and U2R. However, the recall and precision goals are often conflicting and attacking them simultaneously may not work well, especially when some of the classes are rare. This paper presents a novel layered approach with multi-classifier by combining naive bayes classifier (NBC) and naive bayes tree (NBTree) to improve detection rate and precision of minority class without hurting the performance of majority class. We identify important reduced feature set for each attack separately, to form layered approach. The proposed approach scales up the recall and precision for major as well as minor attacks, and keeps the false positives at acceptable level in intrusion detection. (C) 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Department of Computer Science & Engineering, National Institute of Technology Rourkela