Mohammad Othman Nassar

Work place: College of Information Technology, Cyber Security Department, Amman Arab University, Amman, Jordan

E-mail: moanassar@aau.edu.jo

Website:

Research Interests:

Biography

Dr. Mohammed Nassar is an Associate Professor of Cyber Security in Amman Arab University (AAU); he received a Ph.D. in Computer Information System in 2009. Dr. Nassar has 15 years’ teaching experience. He has been working at Amman Arab University since 2010. He occupied different leading positions at Amman Arab University: head of Computer Information System department, eLearning Center Manager, Marketing department Manager, and computer center manager. He published more than 40 scientific research publications. Dr. Nassar is an editorial board member for 4 international scientific journals.

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

By Mohammad Othman Nassar Feras Fares AL-Mashagba

DOI: https://doi.org/10.5815/ijcnis.2025.05.03, Pub. Date: 8 Oct. 2025

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.

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