Privacy Preserving Model for Efficient Designing of Intrusion Detection Systems in IoT Environment: Designing of Intrusion Detection Systems in IoT Environment - Swetha Madireddy; Kalaivani Kathirvelu (International Journal of Computational and Experimental Science and Engineering, 2025)
The swift advancement of the Internet of Things (IoT) has significantly transformed contemporary communication frameworks by facilitating effortless data transmission among a diverse network of interconnected smart devices. Nevertheless, the increased connectivity and resource-constrained nature of IoT nodes have made them prime targets for cyber-attacks, necessitating the development of intelligent and privacy-aware Intrusion Detection Systems (IDS). Traditional IDS approaches often fall short in addressing the dual challenges of real-time threat detection and preserving user data confidentiality. This research represents a Privacy-Preserving Model for Efficient Designing of IDS in IoT Environments by integrating a deep learning-driven detection framework with advanced optimization techniques. The core detection engine is built upon a Gated Recurrent Unit (GRU) network, chosen for its lightweight structure and strong temporal pattern recognition capabilities. To enhance model performance, hyperparameters are tuned using the novel Ladybug Beetle Optimization (LBO) algorithm, which mimics the intelligent foraging behaviour of ladybugs to achieve global optima efficiently. To ensure data privacy and reduce communication overhead, the model is integrated with federated learning, allowing distributed training across IoT devices without centralized data aggregation. Additionally, lightweight encryption techniques are employed to secure data transmission during training and inference phases. The proposed system was evaluated using standard benchmark datasets, achieving a detection accuracy of 98.9%, and the results demonstrate significant improvements in recall, precision and computational efficiency when examined to traditional approaches. This work contributes a scalable, intelligent, and privacy-respecting intrusion detection architecture suitable for real-world IoT scenarios, comprising smart homes, healthcare systems, and industrial automation.