Novel Hybrid LOA-VCS Metaheuristic Approach with Adaptive Parameter Tuning for Network Intrusion Detection

The increasing complexity and dynamism of modern cyber threats necessitate intelligent and adaptive network intrusion detection systems (NIDS). This paper proposes a novel hybrid metaheuristic approach that combines the Lion Optimization Algorithm (LOA) with the Virus Colony Search (VCS), enhanced by adaptive parameter tuning mechanisms. The proposed LOA-VCS hybrid algorithm addresses limitations in prior single and hybrid metaheuristic by alternating exploration and exploitation strategies across epochs, optimizing detection performance in high-dimensional feature spaces. Unlike previous hybrid metaheuristics that use fixed or non-adaptive control, our model uniquely alternates LOA and VCS phases adaptively across epochs to enhance convergence and detection robustness. A real-world intrusion detection dataset evaluated the LOA-VCS model with 98.4% detection accuracy, an F1-score of 0.976, and an AUC of 0.986, consistently outperforming the standalone LOA and VCS baselines. These results emphasize the power of adaptive hybrid met heuristics in maintaining low false alarms while ensuring strong recall for NIDS. The proposed approach can be deployed in scalable, high-speed systems in today’s contemporary cyber security environments.