Intrusion detection in network flows based on an optimized clustering criterion

Authors: JABER KARIMPOUR, SHAHRIAR LOTFI, ALIAKBAR TAJARI SIAHMARZKOOH

Abstract: Graph-based intrusion detection approaches consider the network as a graph and detect anomalies based on graph metrics. However, most of these approaches succumb to the cluster-based behavior of the anomalies. To resolve this problem in our study, we use flow and graph-clustering concepts to create a data set first. A new criterion related to the average weight of clusters is then defined and a model is proposed to detect attacks based on the above-mentioned criterion. Finally, the model is evaluated using a DARPA data set. Results show that the proposed approach detects the attacks with high accuracy relative to methods described in previous studies.

Keywords: Attack, DARPA data set, flow, graph clustering, intrusion detection

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