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Anomaly Extraction in Backbone Networks Using Association Rules

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4 Author(s)
Brauckhoff, D. ; Computing Department, ETH Zurich, Zurich, Switzerland ; Dimitropoulos, X. ; Wagner, A. ; salamatian, K.

Anomaly extraction refers to automatically finding, in a large set of flows observed during an anomalous time interval, the flows associated with the anomalous event(s). It is important for root-cause analysis, network forensics, attack mitigation, and anomaly modeling. In this paper, we use meta-data provided by several histogram-based detectors to identify suspicious flows, and then apply association rule mining to find and summarize anomalous flows. Using rich traffic data from a backbone network, we show that our technique effectively finds the flows associated with the anomalous event(s) in all studied cases. In addition, it triggers a very small number of false positives, on average between 2 and 8.5, which exhibit specific patterns and can be trivially sorted out by an administrator. Our anomaly extraction method significantly reduces the work-hours needed for analyzing alarms, making anomaly detection systems more practical.

Published in:

Networking, IEEE/ACM Transactions on  (Volume:20 ,  Issue: 6 )