<p>This study aims to mitigate cyberphysical risks in an IoT-enabled intelligent transport infrastructure. Self-driving cars (SDCs) are becoming increasingly integral to smart cities, offering significant advantages such as reducing the burden on human drivers, optimizing resource utilization, and contributing to cleaner air through reduced emissions. To address potential cybersecurity challenges, this research presents a novel machine learning (ML)-based intrusion detection system (IDS) targeting an SDC cyberphysical system (CPS) that effectively identifies and mitigates attacks on the associated physical components of SDCs. A key feature of the SDC-CPS architecture is integrating a controller area network into the SDC-related simulator. By leveraging IoT/sensing devices, comprehensive data samples were collected from the SDC-CPS architecture for analysis. Furthermore, the study introduces a krill herd search-driven fine-tuned tree method for data processing, achieving impressive results, including 98.3% precision, 97.8% recall, 98.3% F1-score, and 98.4% accuracy. The findings highlight that the proposed solution outperforms existing IDS models, establishing it as a reliable and efficient approach for enhancing the security of intelligent transport systems.</p>