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Collaboration among IDSs allows users to benefit from the collective knowledge and information from their collaborators and achieve more accurate intrusion detection. However, most existing collaborative intrusion detection networks rely on the exchange of intrusion data which raises the privacy concern of participants. To overcome this problem, the authors propose a knowledge-based intrusion detection network, which provides a platform for IDS users to effectively share their customized detection knowledge in an IDS community. An automatic knowledge propagation mechanism is proposed based on a decentralized two-level optimization problem formulation, leading to a Nash equilibrium solution which is shown to be scalable, incentive compatible, fair, efficient and robust.
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