AI-Driven Intrusion Detection Systems for Low-Power IoT Devices Balancing Accuracy and Efficiency

The rapid proliferation of Internet of Things (IoT) devices has introduced new vectors for cyber threats, particularly due to the limited computational and energy resources these devices typically possess. Traditional intrusion detection systems (IDS), designed for more robust environments, often fail to meet the performance and efficiency demands of constrained IoT networks. This paper explores the design and implementation of AI-driven intrusion detection systems specifically optimized for low-power IoT ecosystems. By integrating lightweight machine learning models with adaptive feature selection techniques, we demonstrate a practical balance between detection accuracy and resource efficiency. Our experimental analysis, conducted across diverse IoT traffic datasets, reveals that targeted model optimization can significantly reduce energy consumption while maintaining high detection precision. We also discuss the implications of edge computing and federated learning in further reducing latency and enhancing system scalability. This study highlights the importance of tailoring AI solutions to the unique limitations of IoT environments, pushing the boundaries of secure, intelligent, and efficient device protection.