Intrusion Detection Systems (IDSs) are designed to monitor network traffic and computer activities in order to alert users about suspicious intrusions. 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 privacy concerns. To overcome this problem, we propose SMURFEN: a Rule Sharing intrusion detection network, which provides a platform for IDS users to effectively share their customized detection knowledge in an IDS community. An automatic rule propagation mechanism is proposed based on a decentralized two-level optimization problem formulation. We evaluate our rule sharing system through simulations and compare our results to existing knowledge sharing methods such as random gossiping and fixed neighbors sharing schemes.
Download Full PDF Version (Non-Commercial Use)