Community-based Collaborative Intrusion Detection

The IT infrastructure of today needs to be ready to defend against massive cyber-attacks which often originate from distributed attackers such as Botnets. Most Intrusion Detection Systems (IDSs), nonetheless, are still working in isolation and cannot effectively detect distributed attacks. Collaborative IDSs (CIDSs) have been proposed as a collaborative defense against the ever more sophisticated distributed attacks. However, collaboration by exchanging suspicious alarms among all interconnected sensors in CIDSs does not scale with the size of the IT infrastructure; hence, detection performance and communication overhead, required for collaboration, must be traded off. We propose to partition the set of considered sensors into subsets, or communities, as a lever for this trade off. The novelty of our approach is the application of ensemble based learning, a machine learning paradigm suitable for distributed intrusion detection. In our approach, community members exchange data features used to train models of normality, not bare alarms, thereby further reducing the communication overhead of our approach. Our experiments show that we can achieve detection rates close to those based on global information exchange with smaller subsets of collaborating sensors