An Adaptive Intrusion Detection Framework for IoT: Balancing Accuracy and Computational Efficiency

Intrusion Detection Systems (IDS) play a critical role in protecting networked environments from cyberattacks. They have become increasingly important in smart environments such as the Internet of Things (IoT) systems. However, IDS for IoT networks face critical challenges due to hardware constraints, including limited computational resources and storage capacity, which lead to high feature dimensionality, prediction uncertainty, and increased processing cost. These factors make many conventional detection approaches unsuitable for real-time IoT deployment. To address these challenges, this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency. The proposed framework integrates mutual information (MI) feature selection model, deep contextual embeddings, and an adaptive decision mechanism. The MI model identifies and retains the most informative features, which reduces dimensionality while maintaining high detection accuracy. The adaptive decision dynamically selects between multiple inference paths to ensure that additional computation is needed only when the uncertainty level is high. Experimental evaluations on benchmark IoT datasets namely RT-IoT-2022, CIC-IoT-2023 and CIC-IoMT-2024 show that the proposed framework achieves F1-score of 99.92%, 96.66%, and 99.84%, respectively, with an average inference time of approximately 0.105 ms per sample. These results demonstrate that the framework effectively adapts inference complexity to data uncertainty, which provides an intelligent, interpretable and efficient solution for real-world IoT intrusion detection.