Online Boosting Based Intrusion Detection in Changing Environments
Yanguo Wang, Weiming Hu, Xiaoqin Zhang · 2014
Intrusion detection is an active research field in the develop-ment of reliable web-based information systems, where many artificial intelligence techniques are exploited to fit the spe-cific application. Although some detection algorithms have been developed, they lack the adaptability to the frequently changing network environments, since they are mostly trained in batch mode. In this paper, we propose an online boosting based intrusion detection method, which has the ability of efficient online learning of new network intrusions. The detection can be performed in real-time with high detection accuracy. Ex-perimental results show the advantage of the method in the intrusion detection application.