Enhancing Intrusion Detection Systems Using Hybrid AI-Based Approaches

Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics. Although numerous machine-learning-based intrusion detection systems (IDS) have been developed, their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency. This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization (PDO) with the exploitation behavior of Ant Colony Optimization (ACO). The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSL-KDD dataset and evaluates them using a Support Vector Machine (SVM) classifier. Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98% while significantly lowering false alarms and computational overhead. Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes, positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.