Automating root-cause analysis of network anomalies using frequent itemset mining
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/14346
Tipus de documentComunicació de congrés
Data publicació2010
EditorACM Press. Association for Computing Machinery
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
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.
CitacióParedes Oliva, Ignasi [et al.]. Automating root-cause analysis of network anomalies using frequent itemset mining. A: ACM SIGCOMM Special Interest Group on Data Communications. "Compilation Proceeding of SIGCOMM 2010 & the Co-Located Workshops". Nova Delhi: ACM Press. Association for Computing Machinery, 2010, p. 467-468.
ISBN978-1-4503-0200-5
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Paredes.pdf | 253,7Kb | Visualitza/Obre |