Intrusion Detection Based on Clustering Organizational Co-Evolutionary Classification
Organizational Co-Evolutionary Classification (OCEC) is a novel classification algorithm, based on co-evolutionary computation. Differing from Genetic Algorithm, OCEC can work without encoding datasets because introducing “organization” concept. To deal with mass data in intrusion detection effectively, we develop a new algorithm, Clustering Organizational Co-Evolutionary Classification (COCEC) by introducing the clustering method to OCEC. COCEC divides initial data into many sections, and each section is considered as an organization, thus COCEC allows more data to obtain evolutionary learning, so the rule set worked out by COCEC contains fewer rules. In addition to improvement of the initial state in OCEC, some improvements have also been done in the choice strategy of the operators and the rule matching method The experiment results show that COCEC is more accurate and more effective than OCEC and OCEFC (Organizational Co-Evolutionary Fuzzy Classification) with the KDD CUP 99 database, and it greatly reduces the number of rules and testing time.
KeywordsTraining Data Intrusion Detection Attribute Significance Attack Type Learning Classifier System
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