M-AdaBoost-A based ensemble system for network intrusion detection

Abstract

Network intrusion detection remains a challenging research area as it involves learning from large-scale imbalanced multiclass datasets. While machine learning algorithms have been widely used for network intrusion detection, most standard techniques cannot achieve consistent good performance across multiple classes. In this paper we proposed a novel ensemble system based on the modified adaptive boosting with area under the curve (M-AdaBoost-A) algorithm to detect network intrusions more effectively. We combined multiple M-AdaBoost-A-based classifiers into an ensemble by employing various strategies, including particle swarm optimization. To the best of our knowledge, this study is the first to utilize the M-AdaBoost-A algorithm, which incorporates the area under the curve into the boosting process for addressing class imbalance in network intrusion detection. Compared with existing standard techniques, our proposed ensemble system achieved superior performance across multiple classes in both 802.11 wireless intrusion detection and traditional enterprise intrusion detection.