Swarm Intelligence-Based Hyperparameter Optimization for AI-Powered IoT Threat Detection
Keywords:
Swarm Intelligence, Hyper parameter Optimization, Internet of Things (IoT), AI-based Threat Detection, Cybersecurity, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO).Abstract
The growing complexity and scale of Internet of Things (IoT) networks demand advanced, intelligent threat detection systems capable of rapid adaptation and high accuracy. This study proposes a novel framework that integrates swarm intelligence-based hyper parameter optimization with AI-powered threat detection models to enhance security in IoT environments. Traditional deep learning models often suffer from suboptimal performance due to manually selected hyperparameters, leading to poor generalization and increased false positives. To address this, swarm intelligence algorithms—such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)—are employed to automatically fine-tune critical hyperparameters, including learning rates, dropout rates, and network depth. These optimized models are then deployed to detect a wide spectrum of IoT-specific threats, such as botnet activity, unauthorized access, and anomaly behavior in sensor data. Experimental evaluations on benchmark IoT security datasets demonstrate significant improvements in detection accuracy, convergence speed, and robustness, compared to baseline models. This approach offers a scalable and adaptive solution for real-time IoT threat detection with reduced human intervention.
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