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In this paper, we propose a novel approach, called SENATUS, for joint trafficanomaly detection and root-cause analysis. Inspired from the concept of asenate, the key idea of the proposed approach is divided into three stages:election, voting and decision. At the election stage, a small number of\nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{,which are used} to represent approximately the total (usually huge) set oftraffic flows. In the voting stage, anomaly detection is applied on the senatorflows and the detected anomalies are correlated to identify the most possibleanomalous time bins. Finally in the decision stage, a machine learningtechnique is applied to the senator flows of each anomalous time bin to findthe root cause of the anomalies. We evaluate SENATUS using traffic tracescollected from the Pan European network, GEANT, and compare against anotherapproach which detects anomalies using lossless compression of traffichistograms. We show the effectiveness of SENATUS in diagnosing anomaly types:network scans and DoS/DDoS attacks.

SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis