Hybrid Metaheuristic-Driven Intrusion Detection System Using Opto-Romar Swarm Bee Genesis Optimization on IoT Network Data

The Internet of Things (IoT) devices have rapidly grown in numbers, posing critical threats in the process of securing networks against emerging cyber-attacks. Traditional Intrusion Detection Systems (IDS) suffer from low accuracy, are not flexible and are inefficient in handling massive multidimensional IoT data. To mitigate these shortcomings, the study presents a new hybrid metaheuristic-based IDS system that combines Cat-Scale normalization technique, the feature selection algorithm: Mutualk-Best, the optimization technique Opto-Romar Swarm Bee Genesis and the hyperparameter optimization algorithm: Evalmax Hyper Net. The framework is developed to provide a balanced representation of features, minimize redundancy, enhance convergence, and learn dynamic parameters to provide robust intrusion detection. Experimental results on the IoT-IDS dataset show the effectiveness of the proposed work. The framework recorded 95% accuracy, 97% precision, 98.6% recall and 98.4% F1-score with an AUC of 0.9999, utilizing better results compared to other techniques of IDS like BESO-HDL and Modified Isolation Forest. These findings support that the combination of swarm intelligence and genetic algorithms, along with adaptive tuning, can present a better detection performance with the ability to scale in highly complex IoT environments. The results indicate the framework as a promising future-proof IDS solution. Future work will consider the deployment of real-world IoT to resource-limited devices, robustness against adversarial attack, and variants to edge and mobile computing tasks.