{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T04:17:03Z","timestamp":1748405823114,"version":"3.41.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Central guidance Local Science and Technology Development Fund Project: Liaoning Province","award":["[20.23] 7-36"],"award-info":[{"award-number":["[20.23] 7-36"]}]},{"name":"Scientific Research Projects of Liaoning Province","award":["LJKMZ20220780"],"award-info":[{"award-number":["LJKMZ20220780"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the rapid spread of the Internet of Vehicles (IoV) technology, vehicle network security is facing increasingly severe challenges. Intrusion detection technology has become a crucial tool for ensuring the information security of IoV. Since the traffic data of the IoV is large and has spatio-temporal characteristics, most previous studies are based on a single deep learning method to extract temporal or spatial features, which does not fully extract features of IoV data. To address the above issues, a spatio-temporal feature extraction model with feature selection is proposed. First, to solve the problem of long detection time with huge data traffic, a new feature selection method is proposed to screen the optimal feature subset by combining the correlation-based feature selection method with the crayfish optimization algorithm (CFS-COA). Second, the selected optimal features are used in a spatio-temporal feature extraction model that combines a Temporal Convolutional Network and a Bidirectional Gated Recurrent Unit (TCN-BiGRU) for classification. Finally, the performance of the model is evaluated using two types of datasets: the NSL-KDD and UNSW-NB15 datasets for external communications, and the Car-Hacking dataset for in-vehicle networks. The experimental results indicate that the proposed model demonstrates high classification performance and lightweight characteristics, achieving 100% accuracy on the Car-Hacking dataset.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae126","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T23:20:49Z","timestamp":1733872849000},"page":"487-501","source":"Crossref","is-referenced-by-count":0,"title":["Internet of vehicles intrusion detection method based on CFS-COA feature selection and spatio-temporal feature extraction"],"prefix":"10.1093","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5549-937X","authenticated-orcid":false,"given":"Zhongjun","family":"Yang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shenyang University of Chemical Technology , Shenyang ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4540-2815","authenticated-orcid":false,"given":"Jixue","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shenyang University of Chemical Technology , Shenyang ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8326-2683","authenticated-orcid":false,"given":"Beimin","family":"Su","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shenyang University of Chemical Technology , Shenyang ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"2025052712495772000_ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3570954","article-title":"Ai-based intrusion detection systems for in-vehicle networks: A survey","volume":"55","author":"Rajapaksha","year":"2023","journal-title":"ACM Comput Surv"},{"key":"2025052712495772000_ref2","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1016\/j.future.2017.12.041","article-title":"Driverless vehicle security: Challenges and future research opportunities","volume":"108","author":"De La Torre","year":"2020","journal-title":"Future Gener Comput Syst"},{"key":"2025052712495772000_ref3","doi-asserted-by":"publisher","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":"2019","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2025052712495772000_ref4","first-page":"1","article-title":"Remote exploitation of an unaltered passenger vehicle","volume":"2015","author":"Miller","year":"2015","journal-title":"Black Hat USA"},{"key":"2025052712495772000_ref5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3472753","article-title":"A survey on data-driven network intrusion detection","volume":"54","author":"Chou","year":"2021","journal-title":"ACM Comput Surv"},{"key":"2025052712495772000_ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/9865549","article-title":"Energy-aware intrusion detection model for internet of vehicles using machine learning methods","volume":"2022","author":"Lihua","year":"2022","journal-title":"Wirel Commun Mob Comput"},{"key":"2025052712495772000_ref7","first-page":"1","volume-title":"2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa Village, USA, 9\u201313 December","author":"Yang","year":"2019"},{"key":"2025052712495772000_ref8","doi-asserted-by":"publisher","first-page":"100471","DOI":"10.1016\/j.vehcom.2022.100471","article-title":"A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic","volume":"35","author":"Lo","year":"2022","journal-title":"Veh Commun"},{"key":"2025052712495772000_ref9","doi-asserted-by":"publisher","first-page":"845","DOI":"10.32604\/cmc.2023.046607","article-title":"A time series intrusion detection method based on SSAE, TCN and Bi-LSTM","volume":"78","author":"He","year":"2024","journal-title":"Comput Mater Contin"},{"key":"2025052712495772000_ref10","doi-asserted-by":"publisher","first-page":"411","DOI":"10.32604\/cmc.2023.046237","article-title":"An industrial intrusion detection method based on hybrid convolutional neural networks with improved TCN","volume":"78","author":"Liu","year":"2024","journal-title":"Comput Mater Contin"},{"key":"2025052712495772000_ref11","doi-asserted-by":"publisher","first-page":"15687","DOI":"10.1109\/TITS.2022.3202869","article-title":"Two-stage intrusion detection system in intelligent transportation systems using rule extraction methods from deep neural networks","volume":"24","author":"Almutlaq","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2025052712495772000_ref12","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.3390\/s22041340","article-title":"HDL-IDS: A hybrid deep learning architecture for intrusion detection in the internet of vehicles","volume":"22","author":"Ullah","year":"2022","journal-title":"Sensors"},{"key":"2025052712495772000_ref13","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.3390\/pr8091055","article-title":"Establish induction motor fault diagnosis system based on feature selection approaches with MRA","volume":"8","author":"Lee","year":"2020","journal-title":"Processes"},{"key":"2025052712495772000_ref14","doi-asserted-by":"publisher","first-page":"71414","DOI":"10.1109\/ACCESS.2022.3186975","article-title":"A hybrid intrusion detection system based on feature selection and weighted stacking classifier","volume":"10","author":"Zhao","year":"2022","journal-title":"IEEE Access"},{"key":"2025052712495772000_ref15","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.1007\/s10462-023-10567-4","article-title":"Crayfish optimization algorithm","volume":"56","author":"Jia","year":"2023","journal-title":"Artif Intell Rev"},{"key":"2025052712495772000_ref16","doi-asserted-by":"publisher","first-page":"103560","DOI":"10.1016\/j.jnca.2022.103560","article-title":"An optimized ensemble prediction model using automl based on soft voting classifier for network intrusion detection","volume":"212","author":"Khan","year":"2023","journal-title":"J Netw Comput Appl"},{"key":"2025052712495772000_ref17","doi-asserted-by":"publisher","first-page":"142206","DOI":"10.1109\/ACCESS.2021.3120626","article-title":"Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset","volume":"9","author":"Gad","year":"2021","journal-title":"IEEE Access"},{"key":"2025052712495772000_ref18","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.3390\/electronics12081757","article-title":"An intelligent intrusion detection system for 5g-enabled internet of vehicles","volume":"12","author":"Sousa","year":"2023","journal-title":"Electronics"},{"key":"2025052712495772000_ref19","doi-asserted-by":"publisher","first-page":"13950","DOI":"10.1109\/JIOT.2021.3069642","article-title":"Machine-learning-based efficient and secure RSU placement mechanism for software-defined-IoV","volume":"8","author":"Anbalagan","year":"2021","journal-title":"IEEE Internet Things J"},{"key":"2025052712495772000_ref20","doi-asserted-by":"publisher","first-page":"18042","DOI":"10.1109\/ACCESS.2017.2747560","article-title":"Network traffic classifier with convolutional and recurrent neural networks for internet of things","volume":"5","author":"Lopez-Martin","year":"2017","journal-title":"IEEE Access"},{"key":"2025052712495772000_ref21","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.dcan.2022.04.021","article-title":"A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder","volume":"9","author":"Wei","year":"2023","journal-title":"Digit Commun Netw"},{"key":"2025052712495772000_ref22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TITS.2022.3141788","article-title":"Communication security analysis of intelligent transportation system using 5G internet of things from the perspective of big data","volume":"24","author":"He","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2025052712495772000_ref23","doi-asserted-by":"publisher","first-page":"15866","DOI":"10.1109\/TITS.2023.3271768","article-title":"IIDS: Intelligent intrusion detection system for sustainable development in autonomous vehicles","volume":"24","author":"Anbalagan","year":"2023","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2025052712495772000_ref24","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.aej.2023.07.077","article-title":"Implementation of African vulture optimization algorithm based on deep learning for cybersecurity intrusion detection","volume":"79","author":"Alsirhani","year":"2023","journal-title":"Alex Eng J"},{"key":"2025052712495772000_ref25","doi-asserted-by":"publisher","first-page":"6295","DOI":"10.1038\/s41598-022-10200-4","article-title":"Multi-attack and multi-classification intrusion detection for vehicle-mounted networks based on mosaic-coded convolutional neural network","volume":"12","author":"Hu","year":"2022","journal-title":"Sci Rep"},{"key":"2025052712495772000_ref26","doi-asserted-by":"publisher","first-page":"120963","DOI":"10.1109\/ACCESS.2023.3328182","article-title":"Transfer learning-based intrusion detection system for a controller area network","volume":"11","author":"Khatri","year":"2023","journal-title":"IEEE Access"},{"key":"2025052712495772000_ref27","doi-asserted-by":"publisher","first-page":"100198","DOI":"10.1016\/j.vehcom.2019.100198","article-title":"In-vehicle network intrusion detection using deep convolutional neural network","volume":"21","author":"Song","year":"2020","journal-title":"Veh Commun"},{"key":"2025052712495772000_ref28","first-page":"012014","volume-title":"2022 3rd International Conference on Signal Processing and Computer Science (SPCS), Qingdao, China, 19\u201321 August","author":"Luo","year":"2022"},{"key":"2025052712495772000_ref29","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1504\/IJBIC.2010.036158","article-title":"Correlation based feature selection method","volume":"2","author":"Michalak","year":"2010","journal-title":"Int J Bio-Inspired Comput"},{"key":"2025052712495772000_ref30","doi-asserted-by":"publisher","first-page":"2984","DOI":"10.3390\/electronics10232984","article-title":"Enhancing big data feature selection using a hybrid correlation-based feature selection","volume":"10","author":"Mohamad","year":"2021","journal-title":"Electronics"},{"key":"2025052712495772000_ref31","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.1007\/s10489-018-1381-1","article-title":"Mrmr+ and Cfs+ feature selection algorithms for high-dimensional data","volume":"49","author":"Angulo","year":"2019","journal-title":"Applied Intelligence"},{"key":"2025052712495772000_ref32","doi-asserted-by":"publisher","first-page":"175793","DOI":"10.1109\/ACCESS.2019.2957662","article-title":"Hybrid multilabel feature selection using BPSO and neighborhood rough sets for multilabel neighborhood decision systems","volume":"7","author":"Sun","year":"2019","journal-title":"Ieee Access"},{"key":"2025052712495772000_ref33","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.neucom.2017.02.053","article-title":"Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier","volume":"241","author":"Mursalin","year":"2017","journal-title":"Neurocomputing"},{"key":"2025052712495772000_ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103465"},{"key":"2025052712495772000_ref35","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2025052712495772000_ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.10.050"},{"key":"2025052712495772000_ref37","first-page":"1","volume-title":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, Ottawa, Canada, 8\u201310 July","author":"Tavallaee","year":"2009"},{"key":"2025052712495772000_ref38","first-page":"1","volume-title":"2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, 10\u201312 November","author":"Moustafa","year":"2015"},{"key":"2025052712495772000_ref39","doi-asserted-by":"publisher","first-page":"e7529","DOI":"10.1002\/cpe.7529","article-title":"An optimized adaptive ensemble model with feature selection for network intrusion detection","volume":"35","author":"Yang","year":"2023","journal-title":"Concurr Comput Pract Exp"},{"key":"2025052712495772000_ref40","doi-asserted-by":"publisher","first-page":"13624","DOI":"10.1109\/ACCESS.2018.2810198","article-title":"An improved intrusion detection algorithm based on GA and SVM","volume":"6","author":"Tao","year":"2018","journal-title":"IEEE Access"},{"key":"2025052712495772000_ref41","first-page":"1","volume-title":"2018 IEEE Student Conference on Research and Development (SCOReD), Selangor, Malaysia, 26\u201328 November","author":"Ali","year":"2018"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/68\/5\/487\/60983049\/bxae126.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/68\/5\/487\/60983049\/bxae126.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T16:50:28Z","timestamp":1748364628000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/68\/5\/487\/7918702"}},"subtitle":[],"editor":[{"given":"Jorge Blasco","family":"Al\u00eds","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,12,7]]},"published-print":{"date-parts":[[2025,5,15]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxae126","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"type":"print","value":"0010-4620"},{"type":"electronic","value":"1460-2067"}],"subject":[],"published-other":{"date-parts":[[2025,5]]},"published":{"date-parts":[[2024,12,7]]}}}