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IoV networks can combine Internet-connected devices to store, process, and analyze real-time data in intelligent transportation. However, detecting cyberattacks in this environment is inevitable and remains a major challenge, as malicious threats can disrupt vehicle communications, leading to network congestion and safety risks. To enhance security in IoV networks, software-defined networking (SDN) provides a centralized and flexible framework for managing traffic flow and implementing security measures. In this study, we propose a novel Intrusion Detection System (IDS) for SDN-enabled IoV environments. Our proposed Genetic Algorithm-Ensemble Bagging Trees (GA-EBT) hybrid model employs the Message Queuing Telemetry Transport (MQTT) protocol for secure data transmission and integrates a hybrid machine-learning model to predict and detect cyber threats in IoV networks. Using the IoT_SDN-IDS and MQTT-IoT-IDS2020 datasets, we complete a comprehensive case study to evaluate various machine learning algorithms. Our findings indicate that our hybrid GA-EBT model performs more effectively than previous models. Simulation results show accuracy up to 99.9931% and 99.997% on the IoT_SDN-IDS and MQTT-IoT-IDS2020 datasets, respectively. The results prove that our hybrid SDN-based cyber-attack detection model effectively detects cyber-attack threats in IoV environments. Moreover, the proposed GA-EBT model provides secure data interaction and improves vehicular communication reliability.<\/jats:p>","DOI":"10.1007\/s10586-025-05929-2","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T20:58:33Z","timestamp":1768078713000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A reliable cyber-attack detection architecture for cyber-physical systems in SDN-enabled internet of vehicles"],"prefix":"10.1007","volume":"29","author":[{"given":"Monire","family":"Norouzi","sequence":"first","affiliation":[]},{"given":"Zeynep","family":"G\u00fcrka\u015f-Ayd\u0131n","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"issue":"22","key":"5929_CR1","doi-asserted-by":"publisher","first-page":"17111","DOI":"10.1007\/s00500-020-05003-6","volume":"24","author":"A Souri","year":"2020","unstructured":"Souri, A., et al.: A new machine learning-based healthcare monitoring model for student\u2019s condition diagnosis in internet of things environment. 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