{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:34:39Z","timestamp":1774629279013,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The Internet of Vehicles (IoV) presents complex cybersecurity challenges, particularly against Denial-of-Service (DoS) and spoofing attacks targeting the Controller Area Network (CAN) bus. This study leverages the CICIoV2024 dataset, comprising six distinct classes of benign traffic and various types of attacks, to evaluate advanced machine learning techniques for instrusion detection systems (IDS). The models XGBoost, Random Forest, AdaBoost, Extra Trees, Logistic Regression, and Deep Neural Network were tested under realistic, imbalanced data conditions, ensuring that the evaluation reflects real-world scenarios where benign traffic dominates. Using hyperparameter optimization with Optuna, we achieved significant improvements in detection accuracy and robustness. Ensemble methods such as XGBoost and Random Forest consistently demonstrated superior performance, achieving perfect accuracy and macro-average F1-scores, even when detecting minority attack classes, in contrast to previous results for the CICIoV2024 dataset. The integration of optimized hyperparameter tuning and a broader methodological scope culminated in an IDS framework capable of addressing diverse attack scenarios with exceptional precision.<\/jats:p>","DOI":"10.3390\/fi17040162","type":"journal-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T20:43:17Z","timestamp":1745440997000},"page":"162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-Class Intrusion Detection in Internet of Vehicles: Optimizing Machine Learning Models on Imbalanced Data"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4450-700X","authenticated-orcid":false,"given":"\u00c1gata","family":"Palma","sequence":"first","affiliation":[{"name":"Institute of Engineering of Coimbra\u2014ISEC, Polytechnic University of Coimbra, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3448-6726","authenticated-orcid":false,"given":"M\u00e1rio","family":"Antunes","sequence":"additional","affiliation":[{"name":"School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal"},{"name":"INESC TEC, CRACS, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9660-2011","authenticated-orcid":false,"given":"Jorge","family":"Bernardino","sequence":"additional","affiliation":[{"name":"Institute of Engineering of Coimbra\u2014ISEC, Polytechnic University of Coimbra, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"CISUC, SSE, University of Coimbra, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3692-338X","authenticated-orcid":false,"given":"Ana","family":"Alves","sequence":"additional","affiliation":[{"name":"Institute of Engineering of Coimbra\u2014ISEC, Polytechnic University of Coimbra, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"CISUC, LASI, University of Coimbra, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100809","DOI":"10.1016\/j.iot.2023.100809","article-title":"Security issues in Internet of Vehicles (IoV): A comprehensive survey","volume":"22","author":"Taslimasa","year":"2023","journal-title":"Internet Things"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108815","DOI":"10.1016\/j.engappai.2024.108815","article-title":"Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles","volume":"135","author":"Gong","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mehedi, S.T., Anwar, A., Rahman, Z., and Ahmed, K. (2021). Deep transfer learning based intrusion detection system for electric vehicular networks. Sensors, 21.","DOI":"10.3390\/s21144736"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"118842","DOI":"10.1109\/ACCESS.2024.3445498","article-title":"A Lightweight Intrusion Detection System for Vehicular Networks Based on an Improved ViT Model","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"99595","DOI":"10.1109\/ACCESS.2021.3095962","article-title":"Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus","volume":"9","author":"Moulahi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1007\/s12083-023-01508-7","article-title":"Machine Learning based intrusion detection systems for connected autonomous vehicles: A survey","volume":"16","author":"Nagarajan","year":"2023","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aloraini, F., Javed, A., and Rana, O. (2024). Adversarial Attacks on Intrusion Detection Systems in In-Vehicle Networks of Connected and Autonomous Vehicles. Sensors, 24.","DOI":"10.3390\/s24123848"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101209","DOI":"10.1016\/j.iot.2024.101209","article-title":"CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus","volume":"26","author":"Neto","year":"2024","journal-title":"Internet Things"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cheng, P., Xu, K., Li, S., and Han, M. (2022). TCAN-IDS: Intrusion Detection System for Internet of Vehicle Using Temporal Convolutional Attention Network. Symmetry, 14.","DOI":"10.3390\/sym14020310"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"El-Gayar, M.M., Alrslani, F.A., and El-Sappagh, S. (2024). Smart Collaborative Intrusion Detection System for Securing Vehicular Networks Using Ensemble Machine Learning Model. Information, 15.","DOI":"10.3390\/info15100583"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/JIOT.2021.3084796","article-title":"MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles","volume":"9","author":"Yang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106285","DOI":"10.1109\/ACCESS.2024.3437416","article-title":"Intrusion Detection System for Vehicular Networks Based on MobileNetV3","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/OJVT.2024.3422253","article-title":"Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks","volume":"5","author":"Almehdhar","year":"2024","journal-title":"IEEE Open J. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Korba, A.A., Sebaa, S., Mabrouki, M., Ghamri-Doudane, Y., and Benatchba, K. (2024, January 27\u201331). A Life-long Learning Intrusion Detection System for 6G-Enabled IoV. Proceedings of the 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024, Ayia Napa, Cyprus.","DOI":"10.1109\/IWCMC61514.2024.10592500"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/JIOT.2023.3318181","article-title":"CVMIDS: Cloud-Vehicle Collaborative Intrusion Detection System for Internet of Vehicles","volume":"11","author":"Qin","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_16","first-page":"2774","article-title":"A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles","volume":"Volume 2022","author":"Yang","year":"2022","journal-title":"Proceedings of the IEEE International Conference on Communications"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gul, M.F., and Bakir, H. (2024, January 21\u201322). Improving Attack Detection in IoV Systems using GA-based Hyperparameter Optimization. Proceedings of the 8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024, Malatya, T\u00fcrkiye.","DOI":"10.1109\/IDAP64064.2024.10711086"},{"key":"ref_18","unstructured":"Teredesai, A., Kumar, V., Li, Y., Rosales, R., Terzi, E., and Karypis, G. (2019, January 4\u20138). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1016\/j.ins.2021.08.010","article-title":"Intrusion detection on internet of vehicles via combining log-ratio oversampling, outlier detection and metric learning","volume":"579","author":"Jin","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1007\/s42979-024-03376-1","article-title":"Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV","volume":"5","author":"Singh","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MNET.2024.3367303","article-title":"Open World Intrusion Detection: An Open Set Recognition Method for CAN Bus in Intelligent Connected Vehicles","volume":"38","author":"Du","year":"2024","journal-title":"IEEE Netw."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ullah, S., Khan, M.A., Ahmad, J., Jamal, S.S., Huma, Z.E., Hassan, M.T., Pitropakis, N., and Buchanan, W.J. (2022). HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles. Sensors, 22.","DOI":"10.3390\/s22041340"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22347","DOI":"10.1007\/s11042-023-15771-6","article-title":"A lightweight intrusion detection system for internet of vehicles based on transfer learning and MobileNetV2 with hyper-parameter optimization","volume":"83","author":"Wang","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/TVT.2024.3402366","article-title":"IoV-BERT-IDS: Hybrid Network Intrusion Detection System in IoV Using Large Language Models","volume":"74","author":"Fu","year":"2024","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.ins.2021.03.042","article-title":"The improved AdaBoost algorithms for imbalanced data classification","volume":"563","author":"Wang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rahman, H.A.A., Wah, Y.B., He, H., and Bulgiba, A. (2015, January 2\u20133). Comparisons of ADABOOST, KNN, SVM and Logistic Regression in Classification of Imbalanced Dataset. Proceedings of the Soft Computing in Data Science, Putrajaya, Malaysia.","DOI":"10.1007\/978-981-287-936-3_6"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/4\/162\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:11:33Z","timestamp":1760029893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/4\/162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,7]]},"references-count":26,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["fi17040162"],"URL":"https:\/\/doi.org\/10.3390\/fi17040162","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,7]]}}}