Sub-Space clustering, Inter-Clustering Results Association & anomaly correlation for unsupervised network anomaly detection

Network anomaly detection is a critical aspect of network management for instance for QoS, security, etc. The continuous arising of new anomalies and attacks create a continuous challenge to cope with events that put the network integrity at risk. Most network anomaly detection systems proposed so far employ a supervised strategy to accomplish the task, using either signature-based detection methods or supervised-learning techniques. However, both approaches present major limitations: the former fails to detect and characterize unknown anomalies (letting the network unprotected for long periods), the latter requires training and labelled traffic, which is difficult and expensive to produce. Such limitations impose a serious bottleneck to the previously presented problem. We introduce an unsupervised approach to detect and characterize network anomalies, without relying on signatures, statistical training, or labelled traffic, which represents a significant step towards the autonomy of networks. Unsupervised detection is accomplished by means of robust data-clustering techniques, combining Sub-Space clustering with Evidence Accumulation or Inter-Clustering Results Association, to blindly identify anomalies in traffic flows. Correlating the results of the unsupervised detection is also performed for improving the detection robustness. Characterization is achieved by building efficient filtering rules to describe a detected anomaly. The detection and characterization performances of the unsupervised approach are evaluated on real network traffic.