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Automating root-cause analysis of network anomalies using frequent itemset mining
Paredes Oliva, Ignasi; Dimitropoulos, Xenofontas; Molina, Maurizio; Barlet Ros, Pere; Brauckhoff, Daniela
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors; Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions de Banda Ampla
Finding the root-cause of a network security anomaly is essential for network operators. In our recent work [1, 5], we introduced a generic technique that uses frequent itemset mining to automatically extract and summarize the traffic flows causing an anomaly. Our evaluation using two different anomaly detectors (including a commercial one) showed that our approach works surprisingly well extracting the anomalous flows in most studied cases using sampled and unsampled NetFlow traces from two networks. In this demonstration, we will showcase an open-source anomaly-extraction system based on our technique, which we integrated with a commercial anomaly detector and use in the NOC of the GÉANT network since late 2009. We will report a number of detected security anomalies and will illustrate how an operator can use our system to automatically extract and summarize anomalous flows.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Computer networks -- Security measures
Anomaly extraction
Anomaly validation
Association rules
Ordinadors, Xarxes d' -- Mesures de seguretat
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
ACM Press. Association for Computing Machinery
         

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