{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T04:46:37Z","timestamp":1782535597634,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious message injection and spoofing can compromise the integrity and availability of vehicular sensing and control functions. Existing deep-learning-based intrusion-detection systems (IDSs) show a clear trade-off: supervised methods perform well on known attacks but rely on costly labels, whereas unsupervised methods can identify unseen attacks but often suffer from high false-positive rates. To address these limitations, this paper proposes a semi-supervised generative adversarial network (SGAN) framework for CAN bus intrusion detection that combines image-based CAN representation with adversarial learning. Consecutive CAN messages are converted into 64\u00d79 grayscale images, and the proposed framework is trained in three phases. First, the discriminator establishes an initial decision boundary using a small labeled subset. It then refines this boundary through distribution-level likelihood objectives and generated samples. Finally, the generator is trained to produce realistic samples capable of deceiving the discriminator. The proposed method was evaluated on the Hacking and Countermeasure Research Lab (HCRL) car-hacking dataset using leave-one-class-out experiments to simulate unknown attacks and achieved an average accuracy of 99.73% and an average F1-score of 99.63% on unknown attacks. Moreover, with only 0.21 M parameters and 3.25 M floating-point operations (FLOPs), the model is well suited for resource-constrained in-vehicle platforms. These results indicate that the proposed framework can serve as a practical cybersecurity component for protecting CAN-carried data in vehicular sensing applications.<\/jats:p>","DOI":"10.3390\/s26123964","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:03:23Z","timestamp":1782263003000},"page":"3964","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0045-8710","authenticated-orcid":false,"given":"Jonggwon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8934-7162","authenticated-orcid":false,"given":"Hyungchul","family":"Im","sequence":"additional","affiliation":[{"name":"Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3879-6438","authenticated-orcid":false,"given":"Semin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3407-6236","authenticated-orcid":false,"given":"Seongsoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/TITS.2014.2320605","article-title":"Intra-vehicle networks: A review","volume":"16","author":"Tuohy","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cinque, E., Valentini, F., Persia, A., Chiocchio, S., Santucci, F., and Pratesi, M. (2020, January 18\u201320). V2X communication technologies and service requirements for connected and autonomous driving. Proceedings of the 2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE), Turin, Italy.","DOI":"10.23919\/AEITAUTOMOTIVE50086.2020.9307388"},{"key":"ref_3","unstructured":"Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., Savage, S., Koscher, K., Czeskis, A., Roesner, F., and Kohno, T. (2011, January 8\u201312). Comprehensive experimental analyses of automotive attack surfaces. Proceedings of the 20th USENIX Security Symposium (USENIX Security 11), San Francisco, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/TIV.2020.2987430","article-title":"Securing vehicle-to-everything (V2X) communication platforms","volume":"5","author":"Hasan","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_5","first-page":"110925","article-title":"Requirements-driven automotive electrical\/electronic architecture: A survey and prospective trends","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/MNET.2018.1700448","article-title":"In-Vehicle Networking: Protocols, Challenges, and Solutions","volume":"33","author":"Huang","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_7","unstructured":"Wolf, M., Weimerskirch, A., and Paar, C. (2004, January 10\u201311). Security in automotive bus systems. Proceedings of the Workshop on Embedded Security in Cars (ESCAR), Bochum, Germany."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Larson, U.E., Nilsson, D.K., and Jonsson, E. (2008, January 4\u20136). An approach to specification-based attack detection for in-vehicle networks. Proceedings of the IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621263"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1109\/TITS.2019.2908074","article-title":"A Survey of Intrusion Detection for In-Vehicle Networks","volume":"21","author":"Wu","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","first-page":"100198","article-title":"In-vehicle network intrusion detection using deep convolutional neural network","volume":"21","author":"Song","year":"2020","journal-title":"Veh. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hossain, M.D., Inoue, H., Ochiai, H., Fall, D., and Kadobayashi, Y. (2020, January 13\u201317). Long Short-Term Memory-Based Intrusion Detection System for In-Vehicle Controller Area Network Bus. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.00011"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Taylor, A., Japkowicz, N., and Leblanc, S.P. (2015, January 14\u201316). Frequency-based anomaly detection for the automotive CAN bus. Proceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS), London, UK.","DOI":"10.1109\/WCICSS.2015.7420322"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, J., Im, H., and Lee, S. (2025, January 19\u201322). Robust Intrusion Detection for In-Vehicle Networks Using a Semi-Supervised GAN Model. Proceedings of the 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall), Chengdu, China.","DOI":"10.1109\/VTC2025-Fall65116.2025.11310645"},{"key":"ref_14","first-page":"100470","article-title":"Rec-CNN: In-vehicle networks intrusion detection using convolutional neural networks trained on recurrence plots","volume":"35","author":"Desta","year":"2022","journal-title":"Veh. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16624","DOI":"10.1109\/TVT.2023.3296705","article-title":"Attack detection for intelligent vehicles via CAN-Bus: A lightweight image network approach","volume":"72","author":"Gao","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aung, Y.L., Cahyadi, W., and Zhou, J. (2025, January 25\u201329). CANDIDS: CAN\/CAN-FD Deep Learning-Based Intrusion Detection Systems. Proceedings of the 11th ACM Cyber-Physical System Security Workshop, Hanoi, Vietnam.","DOI":"10.1145\/3709017.3737713"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17550","DOI":"10.1038\/s41598-025-02474-1","article-title":"A lightweight intrusion detection approach for CAN bus using depthwise separable convolutional Kolmogorov Arnold network","volume":"15","author":"Zhao","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1109\/TNSM.2020.3038991","article-title":"CANnolo: An Anomaly Detection System Based on LSTM Autoencoders for Controller Area Network","volume":"18","author":"Longari","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Seo, E., Song, H.M., and Kim, H.K. (2018, January 28\u201330). GIDS: GAN based Intrusion Detection System for In-Vehicle Network. Proceedings of the 2018 16th Annual Conference on Privacy, Security and Trust, Belfast, UK.","DOI":"10.1109\/PST.2018.8514157"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, D., Im, H., and Lee, S. (2025). Adaptive Autoencoder-Based Intrusion Detection System with Single Threshold for CAN Networks. Sensors, 25.","DOI":"10.3390\/s25134174"},{"key":"ref_21","first-page":"100520","article-title":"Detecting in-vehicle intrusion via semi-supervised learning-based convolutional adversarial autoencoders","volume":"38","author":"Hoang","year":"2022","journal-title":"Veh. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112563","DOI":"10.1016\/j.knosys.2024.112563","article-title":"Semi-supervised intrusion detection system for in-vehicle networks based on variational autoencoder and adversarial reinforcement learning","volume":"304","author":"Nguyen","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"35619","DOI":"10.1109\/JIOT.2025.3579034","article-title":"FSL-IDS: Federated Semi-Supervised Learning Intrusion Detection System for In-Vehicle Networks","volume":"12","author":"Huang","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_24","unstructured":"(2015). Road Vehicles\u2014Controller Area Network (CAN)\u2014Part 1: Data Link Layer and Physical Signalling (Standard No. ISO 11898-1:2015)."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/JPROC.2005.849725","article-title":"Trends in automotive communication systems","volume":"93","author":"Navet","year":"2005","journal-title":"Proc. IEEE"},{"key":"ref_26","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dong, Y., Chen, K., Peng, Y., and Ma, Z. (2022, January 8\u201312). Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network. Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.","DOI":"10.1109\/ITSC55140.2022.9922235"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_30","unstructured":"HCRL (2026, April 13). Car-Hacking Dataset. Available online: https:\/\/ocslab.hksecurity.net\/Datasets\/car-hacking-dataset\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1109\/TVT.2021.3051026","article-title":"Self-supervised anomaly detection for in-vehicle network using noised pseudo normal data","volume":"70","author":"Song","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/JPROC.2020.2976475","article-title":"Model compression and hardware acceleration for neural networks: A comprehensive survey","volume":"108","author":"Deng","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cena, G., Bertolotti, I.C., Hu, T., Valenzano, A., and Zunino, C. (2015, January 16\u201318). Improving compatibility between CAN FD and legacy CAN devices. Proceedings of the 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry (RTSI), Turin, Italy.","DOI":"10.1109\/RTSI.2015.7325134"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3964\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T04:17:05Z","timestamp":1782533825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3964"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123964"],"URL":"https:\/\/doi.org\/10.3390\/s26123964","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,22]]}}}