A Distributed Intrusion Detection Framework Based on Evolved Specialized Ensembles of Classifiers

Modern intrusion detection systems must handle many complicated issues in real-time, as they have to cope with a real data stream; indeed, for the task of classification, typically the classes are unbalanced and, in addition, they have to cope with distributed attacks and they have to quickly react to changes in the data. Data mining techniques and, in particular, ensemble of classifiers permit to combine different classifiers that together provide complementary information and can be built in an incremental way. This paper introduces the architecture of a distributed intrusion detection framework and in particular, the detector module based on a meta-ensemble, which is used to cope with the problem of detecting intrusions, in which typically the number of attacks is minor than the number of normal connections. To this aim, we explore the usage of ensembles specialized to detect particular types of attack or normal connections, and Genetic Programming is adopted to generate a non-trainable function to combine each specialized ensemble. Non-trainable functions can be evolved without any extra phase of training and, therefore, they are particularly apt to handle concept drifts, also in the case of real-time constraints. Preliminary experiments, conducted on the well-known KDD dataset and on a more up-to-date dataset, ISCX IDS, show the effectiveness of the approach.