NAIDS4IoT: A Novel Artificial Intelligence-Based Intrusion Detection Architecture for the Internet of Things

Internet of Things (IoT) has brought unprecedented opportunities across various sectors, including healthcare, transportation, industrial automation, and smart cities. However, this expansion has also introduced significant security vulnerabilities due to the heterogeneous nature, limited computational capabilities, and large-scale deployment of IoT devices. Detecting anomalies, which often signify security breaches or system malfunctions, is crucial to maintaining the integrity and reliability of IoT systems. Traditional anomaly detection methods, typically rule based or signature driven, struggle to adapt to evolving threats and diverse data patterns in IoT networks. This paper proposes a novel architecture named NAIIDS4IoT (Novel Artificial Intelligence-based Intrusion Detection System architecture for IoT), designed to provide efficient, accurate, and scalable anomaly detection using Artificial Intelligence. The core of NAIIDS4IoT lies in the integration of federated learning with deep autoencoders, enabling decentralized model training across edge devices without sharing raw data, thereby preserving user privacy and reducing communication overhead. Each edge node independently learns patterns of normal behavior and identifies anomalies based on reconstruction errors. A global model is continuously refined through collaborative learning across nodes. Furthermore, NAIIDS4IoT incorporates lightweight encryption and blockchain based model integrity verification to enhance security and trust in the detection process. Experimental validation using real-world IoT datasets demonstrates that NAIIDS4IoT achieves high detection accuracy, low false positive rates, and strong adaptability to dynamic environments, significantly outperforming conventional centralized and shallow learning based solutions. This architecture represents a significant step toward intelligent, autonomous, and privacy-preserving anomaly detection in next generation IoT ecosystems.