Bayesian decision aggregation in collaborative intrusion detection networks

—Cooperation between intrusion detection systems (IDSs) allows collective information and experience from a network of IDSs to be shared to improve the accuracy of detection. A critical component of a collaborative network is the mechanism of feedback aggregation in which each IDS makes an overall security evaluation based on peers opinion and assessment. In this paper, we propose a collaboration framework for intrusion detection networks (CIDNs) and use a Bayesian approach for feedback aggregation by minimizing cost. The proposed model is highly scalable, robust, and cost effective. Experimental results demonstrate an improvement in the true positive detection rate and a reduction in the average cost of our mechanism compared to existing models.

• Type: Journal
• Year: 2010
• Where: NOMS
• Authors: Carol J. Fung, Quanyan Zhu, Raouf Boutaba, Tamer Basar
• Keywords: Communications, Feedback Aggregation, Intrusion Detection, Intrusion Detection Systems, NOMS 2010