Trust-Based Classifier Combination for Network Anomaly Detection

  • Authors:
  • Martin Rehák;Michal Pěchouček;Martin Grill;Karel Bartos

  • Affiliations:
  • Department of Cybernetics and Center for Applied Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic 166 27;Department of Cybernetics and Center for Applied Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic 166 27;Department of Cybernetics and Center for Applied Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic 166 27 and CESNET, z. s. p. o., Prague ...;Department of Cybernetics and Center for Applied Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic 166 27 and CESNET, z. s. p. o., Prague ...

  • Venue:
  • CIA '08 Proceedings of the 12th international workshop on Cooperative Information Agents XII
  • Year:
  • 2008

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Abstract

We present a method that improves the results of network intrusion detection by integrating several anomaly detection algorithms through trust and reputation models. Our algorithm is based on existing network behavior analysis approaches that are embodied into several detection agents. We divide the processing into three distinct phases: anomaly detection, trust model update and collective trusting decision. Each of these phases contributes to the reduction of classification error rate, by the aggregation of anomaly values provided by individual algorithms, individual update of each agent's trust model based on distinct traffic representation features (derived from its anomaly detection model), and re-aggregation of the trustfulness data provided by individual agents. The result is a trustfulness score for each network flow, which can be used to guide the manual inspection, thus significantly reducing the amount of traffic to analyze. To evaluate the effectiveness of the method, we present a set of experiments performed on real network data.