A novel PCA-based Network Anomaly Detection

The increasing number of network attacks causes growing problems for network operators and users. Thus, detecting anomalous traffic is of primary interest in IP networks management. In this paper we address the problem considering a method based on PCA for detecting network anomalies. In more detail, we present a new technique that extends the state of the art in PCA based anomaly detection. Indeed, by means of the Kullback-Leibler divergence we are able to obtain great improvements with respect to the performance of the "classical" approach. Moreover we also introduce a method for identifying the flows responsible for an anomaly detected at the aggregated level. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed method.