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We apply advanced agent trust modeling techniques to identify malicious traffic in computer networks. Our work integrates four state-of-the-art techniques from anomaly detection, and combines them by means of extended trust model. Deployment of trust model ensures interoperability between methods, allows cross-correlation of results during various stages of the detection and ensures efficient evaluation of current traffic in the context of historical observations. The goal of the system, which is designed for online monitoring of high-speed network, is to provide efficient tool for targeted runtime surveillance of malicious traffic by network operators. We aim to achieve this objective by filtering out the non-malicious (trusted) part of the traffic and submitting only potentially malicious flows for subsequent semi-automatic inspection.