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, this paper presents a new technique that extends the state of the art in PCA-based anomaly detection. Indeed, by means of multi-scale analysis and 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
Improving PCA-based anomaly detection by using multiple time scale analysis and Kullback-Leibler divergence
CALLEGARI, CHRISTIAN;GAZZARRINI, LORIS;GIORDANO, STEFANO;PAGANO, MICHELE;
2014-01-01
Abstract
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, this paper presents a new technique that extends the state of the art in PCA-based anomaly detection. Indeed, by means of multi-scale analysis and 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 methodI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
