Hierarchical two-tier ensemble learning

Abstract

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Intrusion detection is a mechanism of providing security to computer networks. Almost all of traditional intelligent intrusion detection systems (IDSs) use a single approach to distinguish normal behavior patterns from attack signatures. Moreover these systems have a high false alarm rate and high cost. The combination of multiple classifiers usually exhibits lower false alarm and overall error rate than individual decisions. On the other hand, the combination of classifiers trained on different feature sets could provide better performances than each single classifier. In this paper, a hierarchical two-level combiner is proposed to detect network intrusions using multiple well-known and efficient base classifiers. The proposed combiner exploits the different recognition capabilities provided by the independent feature representations in the first level as well as the agreement among the classifiers in the second level.