Acquaintance Management Algorithm Based on the Multi-Class Risk-Cost Analysis for Collaborative Intrusion Detection Network

Abstract

The collaborative intrusion detection network (CIDN) framework provides collaboration capability among intrusion detection systems (IDS). Collaboration selection is done by an acquaintance management algorithm. A recent study developed an effective acquaintance management algorithm by the use of binary risk analysis and greedy-selection-sort based methods. However, most algorithms do not pay attention to the possibility of wrong responses in multi-botnet attacks. The greedy-based acquaintance management algorithm also leads to a poor acquaintance selection processing time when there is a high number of IDS candidates. The growing number of advanced distributed denial of service (DDoS) attacks make acquaintance management potentially end up with an unreliable CIDN acquaintance list, resulting in low decision accuracy. This paper proposes an acquaintance management algorithm based on multi-class risk-cost analysis and merge-sort selection methods. The algorithm implements merge risk-ordered selection to reduce computation complexity. The simulation result showed the reliability of CIDN in reducing the acquaintance selection processing time decreased and increasing the decision accuracy.