{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:17:12Z","timestamp":1792174632715,"version":"4.4.0"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mu Han","award":["BE2017035"],"award-info":[{"award-number":["BE2017035"]}]},{"name":"Mu Han","award":["1281170019"],"award-info":[{"award-number":["1281170019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Intrusion detection systems based on recurrent neural network (RNN) have been considered as one of the effective methods to detect time-series data of in-vehicle networks. However, building a model for each arbitration bit is not only complex in structure but also has high computational overhead. Convolutional neural network (CNN) has always performed excellently in processing images, but they have recently shown great performance in learning features of normal and attack traffic by constructing message matrices in such a manner as to achieve real-time monitoring but suffer from the problem of temporal relationships in context and inadequate feature representation in key regions. Therefore, this paper proposes a temporal convolutional network with global attention to construct an in-vehicle network intrusion detection model, called TCAN-IDS. Specifically, the TCAN-IDS model continuously encodes 19-bit features consisting of an arbitration bit and data field of the original message into a message matrix, which is symmetric to messages recalling a historical moment. Thereafter, the feature extraction model extracts its spatial-temporal detail features. Notably, global attention enables global critical region attention based on channel and spatial feature coefficients, thus ignoring unimportant byte changes. Finally, anomalous traffic is monitored by a two-class classification component. Experiments show that TCAN-IDS demonstrates high detection performance on publicly known attack datasets and is able to accomplish real-time monitoring. In particular, it is anticipated to provide a high level of symmetry between information security and illegal intrusion.<\/jats:p>","DOI":"10.3390\/sym14020310","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:40:18Z","timestamp":1644180018000},"page":"310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["TCAN-IDS: Intrusion Detection System for Internet of Vehicle Using Temporal Convolutional Attention Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0945-8790","authenticated-orcid":false,"given":"Pengzhou","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pawelec, K., Bridges, R.A., and Combs, F.L. (2019, January 27). Towards a CAN IDS based on a neural network data field predictor. Proceedings of the ACM Workshop on Automotive Cybersecurity, Richardson, TX, USA.","DOI":"10.1145\/3309171.3309180"},{"key":"ref_2","first-page":"100291","article-title":"Application of Controller Area Network (CAN) bus anomaly detection based on time series prediction","volume":"27","author":"Qin","year":"2021","journal-title":"Veh. Commun."},{"key":"ref_3","first-page":"2021","article-title":"Comprehensive Experimental Analyses of Automotive Attack Surfaces","volume":"Volume 4","author":"Checkoway","year":"2011","journal-title":"Proceedings of the USENIX Security Symposium"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101857","DOI":"10.1016\/j.cose.2020.101857","article-title":"CAN-ADF: The controller area network attack detection framework","volume":"94","author":"Tariq","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s11554-021-01110-1","article-title":"Nighttime object detection system with lightweight deep network for internet of vehicles","volume":"18","author":"Jhong","year":"2021","journal-title":"J. Real-Time Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3431233","article-title":"Cyberattacks and countermeasures for in-vehicle networks","volume":"54","author":"Aliwa","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","first-page":"S91","article-title":"Remote exploitation of an unaltered passenger vehicle","volume":"2015","author":"Miller","year":"2015","journal-title":"Black Hat USA"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fowler, D.S., Bryans, J., Shaikh, S.A., and Wooderson, P. (2018, January 25\u201328). Fuzz testing for automotive cyber-security. Proceedings of the 2018 48th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), Luxembourg.","DOI":"10.1109\/DSN-W.2018.00070"},{"key":"ref_9","unstructured":"Rouf, I., Miller, R.D., Mustafa, H.A., Taylor, T., Oh, S., Xu, W., Gruteser, M., Trappe, W., and Seskar, I. (2010). Security and Privacy Vulnerabilities of In-Car Wireless Networks: A Tire Pressure Monitoring System Case Study. Proceedings of the USENIX Security Symposium, USENIX Association."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jaw, E., and Wang, X. (2021). Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach. Symmetry, 13.","DOI":"10.3390\/sym13101764"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/j.procs.2021.04.014","article-title":"Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey","volume":"184","author":"Bangui","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101804","DOI":"10.1016\/j.flowmeasinst.2020.101804","article-title":"Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter","volume":"75","author":"Roshani","year":"2020","journal-title":"Flow Meas. Instrum."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sanaat, A., and Zaidi, H. (2020). Depth of interaction estimation in a preclinical PET scanner equipped with monolithic crystals coupled to SiPMs using a deep neural network. Appl. Sci., 10.","DOI":"10.3390\/app10144753"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108474","DOI":"10.1016\/j.measurement.2020.108474","article-title":"Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique","volume":"168","author":"Sattari","year":"2021","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6395","DOI":"10.1038\/s41598-021-85683-8","article-title":"A cautionary tale for machine learning generated configurations in presence of a conserved quantity","volume":"11","author":"Azizi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_17","first-page":"1879","article-title":"A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics","volume":"66","author":"Alotaibi","year":"2021","journal-title":"CMC Comput. Mater. Contin."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shrestha, R., Omidkar, A., Roudi, S.A., Abbas, R., and Kim, S. (2021). Machine-learning-enabled intrusion detection system for cellular connected UAV networks. Electronics, 10.","DOI":"10.3390\/electronics10131549"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Subramani, N., Mohan, P., Alotaibi, Y., Alghamdi, S., and Khalaf, O.I. (2022). An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks. Sensors, 22.","DOI":"10.3390\/s22020415"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/JIOT.2021.3084796","article-title":"MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kang, M.J., and Kang, J.W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0155781"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhong, M., Zhou, Y., and Chen, G. (2021). Sequential model based intrusion detection system for IoT servers using deep learning methods. Sensors, 21.","DOI":"10.3390\/s21041113"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10880","DOI":"10.1109\/TVT.2021.3106940","article-title":"Anomaly Detection for In-Vehicle Network Using CNN-LSTM With Attention Mechanism","volume":"70","author":"Sun","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_24","unstructured":"Tariq, S., Lee, S., and Woo, S.S. (April, January 30). CANTransfer: Transfer learning based intrusion detection on a controller area network using convolutional LSTM network. Proceedings of the 35th Annual ACM Symposium on Applied Computing, Gwangju, Korea."},{"key":"ref_25","unstructured":"Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., and Chawla, N.V. (February, January 27). A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"59527","DOI":"10.1109\/ACCESS.2021.3073413","article-title":"An Ensemble Intrusion Detection Method for Train Ethernet Consist Network Based on CNN and RNN","volume":"9","author":"Yue","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_28","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3913515","DOI":"10.1155\/2021\/3913515","article-title":"Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network","volume":"2021","author":"Yue","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_30","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 (PST), Belfast, Ireland.","DOI":"10.1109\/PST.2018.8514157"},{"key":"ref_31","first-page":"100374","article-title":"PPM-InVIDS: Privacy protection model for in-vehicle intrusion detection system based complex-valued neural network","volume":"31","author":"Han","year":"2021","journal-title":"Veh. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Taylor, A., Leblanc, S., and Japkowicz, N. (2016, January 17\u201319). Anomaly detection in automobile control network data with long short-term memory networks. Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, Canada.","DOI":"10.1109\/DSAA.2016.20"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, Z., Li, F., Xie, C., Ren, T., Chen, J., and Liu, L. (2019). A deep learning framework for driving behavior identification on in-vehicle CAN-BUS sensor data. Sensors, 19.","DOI":"10.3390\/s19061356"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/2\/310\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:13:23Z","timestamp":1760134403000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/2\/310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,3]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["sym14020310"],"URL":"https:\/\/doi.org\/10.3390\/sym14020310","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,3]]}}}