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.

In modern automotive systems, introducing multiple connectivity protocols has transformed in-vehicle network communication, resulting in the widely recognized Controller Area Network (CAN) standard. Despite its ubiquitous use, the CAN protocol lacks critical security features, making vehicle communications vulnerable to message injection attacks. These assaults might confuse original electronic control units (ECUs) or cause system failures, emphasizing the need for strong cybersecurity solutions in automobile networks. This study addresses this need by developing a quick and efficient abnormal traffic detection system to protect vehicular communications from cyber attacks. The proposed system utilizes four machine learning techniques: Adaboost Trees (ABT), Coarse Decision Trees (CDT), Naive Bayes Classifier (NBC), and Support Vector Machine (SVM). These models were carefully assessed on the Car-Hacking-2018 dataset, which simulates real-time vehicular communication scenarios. Specifically, the system considers five balanced classes, including one normal traffic class and four classes for message injection attacks over the in-vehicle controller area network: fuzzy attack, DoS attack, RPM attack (spoofing), and gear attack (spoofing). Our best performance outcomes belong to the ABT model, which notched 99.8% classification accuracy and 6.67 µs of classification overhead. Such results have outweighed existing in-vehicle intrusion detection systems employing the same/similar dataset.