Optimizing AI-Driven Security Protocols in IoT Networks Using Metaheuristic Algorithms
Keywords:
IoT Security, Artificial Intelligence, Metaheuristic Algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Anomaly Detection, Intrusion Prevention, AI Optimization, Smart Networks, Adaptive Security, Cybersecurity in IoT, Resource-Constrained Devices, Secure Communication, Intelligent Protocols.Abstract
The exponential growth of Internet of Things (IoT) networks has introduced unprecedented challenges in securing heterogeneous and resource-constrained devices against evolving cyber threats. This research proposes a novel framework that integrates Artificial Intelligence (AI)-driven security protocols with metaheuristic optimization techniques to enhance the resilience and efficiency of IoT networks. The AI models, including lightweight neural networks and anomaly detection systems, are designed to identify intrusion patterns and unauthorized behavior in real time. To improve the adaptability and performance of these models, metaheuristic algorithms—such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—are employed to optimize hyperparameters, rule sets, and decision thresholds within the security protocols. The multi-objective optimization process considers key factors such as detection accuracy, energy consumption, latency, and false positive rate. Experimental results on standard IoT benchmark datasets demonstrate that the optimized AI-security protocol framework significantly outperforms traditional approaches in both detection speed and resource efficiency. This work contributes a robust, scalable, and intelligent defense mechanism for next-generation IoT infrastructures.
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