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Hunting Attacks in the Dark: Clustering and Correlation Analysis for Unsupervised Anomaly Detection

Abstract : Network anomalies and attacks represent a serious challenge to ISPs, who need to cope with an increasing number of unknown events that put their networks' integrity at risk. Most of the network anomaly detection systems proposed so far employ a supervised strategy to accomplish their task, using either signature-based detection methods or supervised-learning techniques. The former fails to detect unknown anomalies, exposing the network to severe consequences; the latter requires labeled traffic, which is difficult and expensive to produce. In this paper we introduce a powerful unsupervised approach to detect and characterize network anomalies in the dark, i.e., without relying on signatures or labeled traffic. Unsupervised detection is accomplished by means of robust clustering techniques , combining sub-space clustering with correlation analysis to blindly identify anomalies. To alleviate network operator's post-processing tasks and to speed-up the deployment of effective countermeasures, anomaly ranking and characterization are automatically performed on the detected events. The system is extensively tested with real traffic from the WIDE backbone network, spanning six years of flows captured from a trans-pacific link between Japan and the US, using the MAWILab framework for ground-truth generation. We additionally evaluate the proposed approach with synthetic data, consisting of traffic from an operational network with synthetic attacks. Finally, we compare the performance of the unsupervised detection against different previously used unsupervised detection techniques, as well as against multiple anomaly detectors used in MAWILab.
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https://hal.laas.fr/hal-01927394
Contributor : Philippe Owezarski <>
Submitted on : Monday, November 19, 2018 - 6:34:15 PM
Last modification on : Thursday, June 10, 2021 - 3:07:01 AM
Long-term archiving on: : Wednesday, February 20, 2019 - 3:54:30 PM

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Johan Mazel, Pedro Casas, Romain Fontugne, K. Fukuda, Philippe Owezarski. Hunting Attacks in the Dark: Clustering and Correlation Analysis for Unsupervised Anomaly Detection. International Journal of Network Management, Wiley-Blackwell, 2015, Measure, Detect and Mitigate ‐ Challenges and Trends in Network Security, 25 (5), pp.283-305. ⟨10.1002/nem.1903⟩. ⟨hal-01927394⟩

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