An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance
Thiago José Lucas, Kelton Augusto Pontara da Costa, Rafał Scherer, João Paulo Papa · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
Machine learning techniques have achieved promising results in detecting attacks in computer networks, particularly ensemble learning methods, improving individual classifier’s performance. This work focuses on building an ensemble of classifiers to minimize the computational cost to some extent. A diversity-driven pruning method was applied to create stackings using a combination of k-Nearest Neighbors, Decision Trees, Support Vector Machines, and Neural Networks, and validated on six differents datasets. An average accuracy of 99.94% and a reduction in the processing time of 97.34% are reported with heterogeneous ensembles, highlighting the robustness of the proposed approach.