Intrusion detection in computer networks by a modular ensemble of one-class classifiers

Since the early days of research on Intrusion Detection, anomaly-based approaches have been proposed to detect intrusion attempts. Attacks are detected as anomalies when compared to a model of normal (legitimate) events. Anomaly-based approaches typically produce a relatively large number of false alarms compared to signature-based IDS. However, anomaly-based IDS are able to detect never-before-seen attacks. As new types of attacks are generated at an increasing pace and the process of signature generation is slow, it turns out that signature-based IDS can be easily evaded by new attacks. The ability of anomaly-based IDS to detect attacks never observed in the wild has stirred up a renewed interest in anomaly detection. In particular, recent work focused on unsupervised or unlabeled anomaly detection, due to the fact that it is very hard and expensive to obtain a labeled dataset containing only pure normal events. The unlabeled approaches proposed so far for network IDS focused on modeling the normal network traffic considered as a whole. As network traffic related to different protocols or services exhibits different characteristics, this paper proposes an unlabeled Network Anomaly IDS based on a modular Multiple Classifier System (MCS). Each module is designed to model a particular group of similar protocols or network services. The use of a modular MCS allows the designer to choose a different model and decision threshold for different (groups of) network services. This also allows the designer to tune the false alarm rate and detection rate produced by each module to optimize the overall performance of the ensemble. Experimental results on the KDD-Cup 1999 dataset show that the proposed anomaly IDS achieves high attack detection rate and low false alarm rate at the same time.