A Hybrid Two-Step-Model Based on Cuckoo-Search and Grey Wolf Optimiser Ensemble-Based Classifier Approach for Network Intrusion Detection

Abstract The increasing sophistication of internet threats necessitates advanced intrusion detection systems. This research proposes an ensemble-based Intrusion Detection System that leverages a two-tier feature selection process using Cuckoo Search and Grey Wolf Optimizer algorithms to identify the most discriminative features. For classification, a voting ensemble integrates five machine learning algorithms—Fuzzy Unordered Rule Induction Algorithm, Voted Perceptron, Simple Logistic, Forest PA, and C4.5—to enhance detection accuracy. Results demonstrate that the proposed ensemble model, utilizing a maximum probability voting rule, achieves superior performance compared to any individual classifier.