Ensemble based collaborative and distributed intrusion detection systems: A survey

Modern network intrusion detection systems must be able to handle large and fast changing data, often also taking into account real-time requirements. Ensemble-based data mining algorithms and their distributed implementations are a promising approach to these issues. Therefore, this work presents the current state of the art of the ensemble-based methods used in modern intrusion detection systems, with a particular attention to distributed approaches and implementations. This review also consider supervised and unsupervised data mining algorithms, more suitable to work in an environment that requires the analysis of data streams in real-time. Sharing knowledge across multiple nodes is another of the key points in designing appropriate NIDSs and for this reason, collaborative IDS were also included in this work. Finally, we discuss some open issues and lessons learned from this review, which can help researchers to design more efficient NIDSs.