AI-Assisted IoT Network Security using Hybrid Metaheuristic Optimization Algorithm (AI-HMOA)

Abstract

The intensive growth of Internet of Things (IoT) devices has caused significant security concerns because of dynamic networks structure, resource scarcity, and differences in devices, as well as rising vulnerability to advanced cyberattacks. The traditional security control systems are not always able to offer such flexible and energy efficient security in large scale IoT systems. To overcome these issues, this paper presents an AI-based IoT network security architecture based on hybrid metaheuristic optimization algorithms (AI-HMOA) that intelligently optimizes the resistance to intrusion, secure routing and key management operations. The presented framework will combine the use of machine learning-based attack detection and a hybrid metaheuristic optimization framework that incorporates global search algorithm exploration and local search strategy exploitation. The hybrid optimizer operates dynamically to choose the best security parameters such as the use of secure routes, cryptographic keys, and trust levels of the nodes to use, depending on the current network conditions. Also, adaptive learning regarding traffic patterns through AI-driven decision-making can be used to identify deviant behavior and avert threats like packet dropping, spoofing, and denial-of-service attacks. Simulation experiments have shown that the suggested solution qualifies greatly in terms of improving the ratio of packet delivery, network life and detection accuracy and minimizing energy consumption, routing overhead and the effects of attacks as compared to traditional and single-algorithm optimization based security systems. The results validate the fact that a combination of artificial intelligence and hybrid metaheuristic optimization provides a powerful, scalable, and energy-efficient security solution of the next-generation IoT network.