{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:52:03Z","timestamp":1780087923745,"version":"3.54.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The rapid expansion of intra-vehicle networks has increased the number of threats to such networks. Most modern vehicles implement various physical and data-link layer technologies. Vehicles are becoming increasingly autonomous and connected. Controller area network (CAN) is a serial bus system that is used to connect sensors and controllers (electronic control units\u2014ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. The goal of this research is to design, implement, and test an efficient and effective intrusion detection system for intra-vehicle CANs. Classic cryptographic approaches are resource-intensive and increase processing delay, thereby not meeting CAN latency requirements. There is a need for a system that is capable of detecting intrusions in almost real-time with minimal resources. Our research proposes a long short-term memory (LSTM) network to detect anomalies and a decision engine to detect intrusions by using multiple contextual parameters. We have tested our anomaly detection algorithm and our decision engine using data from real automobiles. We present the results of our experiments and analyze our findings. After detailed evaluation of our system, we believe that we have designed a vehicle security solution that meets all the outlined requirements and goals.<\/jats:p>","DOI":"10.1186\/s42400-023-00195-4","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T01:07:15Z","timestamp":1706836035000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Intrusion detection system for controller area network"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5775-6146","authenticated-orcid":false,"given":"Vinayak","family":"Tanksale","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"195_CR1","doi-asserted-by":"publisher","first-page":"58621","DOI":"10.1109\/ACCESS.2021.3073057","volume":"9","author":"M Bozdal","year":"2021","unstructured":"Bozdal M, Samie M, Jennions IK (2021) Winds: a wavelet-based intrusion detection system for controller area network (can). IEEE Access 9:58621\u201358633. https:\/\/doi.org\/10.1109\/ACCESS.2021.3073057","journal-title":"IEEE Access"},{"issue":"3","key":"195_CR2","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297. https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach Learn"},{"issue":"4","key":"195_CR3","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1109\/TC.2013.13","volume":"63","author":"G Creech","year":"2014","unstructured":"Creech G, Hu J (2014) A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns. IEEE Trans Comput 63(4):807\u2013819","journal-title":"IEEE Trans Comput"},{"key":"195_CR4","doi-asserted-by":"crossref","unstructured":"Cristianini N, Shawe-Taylor J, Shawe-Taylor DCSRHJ, Books24x7 I, Press CU (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, USA. https:\/\/books.google.com\/books?id=_PXJn_cxv0AC","DOI":"10.1017\/CBO9780511801389"},{"key":"195_CR5","doi-asserted-by":"crossref","unstructured":"Desta AK, Ohira S, Arai I, Fujikawa K (2020) Id sequence analysis for intrusion detection in the can bus using long short term memory networks. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp 1\u20136","DOI":"10.1109\/PerComWorkshops48775.2020.9156250"},{"issue":"4","key":"195_CR6","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.dcan.2020.04.007","volume":"6","author":"M Dibaei","year":"2020","unstructured":"Dibaei M, Zheng X, Jiang K, Abbas R, Liu S, Zhang Y, Xiang Y, Yu S (2020) Attacks and defences on intelligent connected vehicles: a survey. Digit Commun Netw 6(4):399\u2013421. https:\/\/doi.org\/10.1016\/j.dcan.2020.04.007","journal-title":"Digit Commun Netw"},{"key":"195_CR7","doi-asserted-by":"publisher","unstructured":"Dibaei M, Zheng X, Jiang K, Maric S, Abbas R, Liu S, Zhang Y, Deng Y, Wen S, Zhang J, Xiang Y, Yu S (2019) An overview of attacks and defences on intelligent connected vehicles. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1907.07455. arXiv:1907.07455","DOI":"10.48550\/ARXIV.1907.07455"},{"key":"195_CR8","doi-asserted-by":"publisher","unstructured":"Dupont G, Lekidis A, Hartog JJ, Etalle SS (2019) Automotive Controller Area Network (CAN) Bus Intrusion Dataset v2. 4TU.ResearchData. https:\/\/doi.org\/10.4121\/uuid:b74b4928-c377-4585-9432-2004dfa20a5d . https:\/\/data.4tu.nl\/articles\/dataset\/Automotive_Controller_Area_Network_CAN_Bus_Intrusion_Dataset\/12696950\/2","DOI":"10.4121\/uuid:b74b4928-c377-4585-9432-2004dfa20a5d"},{"key":"195_CR9","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1007\/978-3-319-66402-6_27","volume-title":"Computer Security - ESORICS 2017","author":"S Fr\u00f6schle","year":"2017","unstructured":"Fr\u00f6schle S, St\u00fchring A (2017) Analyzing the capabilities of the can attacker. In: Foley SN, Gollmann D, Snekkenes E (eds) Computer Security - ESORICS 2017. Springer, Cham, pp 464\u2013482"},{"issue":"6","key":"195_CR10","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1016\/j.jnca.2009.05.004","volume":"32","author":"XD Hoang","year":"2009","unstructured":"Hoang XD, Hu J, Bertok P (2009) A program-based anomaly intrusion detection scheme using multiple detection engines and fuzzy inference. J Netw Comput Appl 32(6):1219\u20131228. https:\/\/doi.org\/10.1016\/j.jnca.2009.05.004","journal-title":"J Netw Comput Appl"},{"key":"195_CR11","unstructured":"Hoppe T, Dittman J (2007) Sniffing\/replay attacks on can buses: a simulated attack on the electric window lift classified using an adapted cert taxonomy. In: Proceedings of the 2nd workshop on embedded systems security (WESS), pp 1\u20136"},{"key":"195_CR12","doi-asserted-by":"crossref","unstructured":"Hossain MD, Inoue H, Ochiai H, Fall D, Kadobayashi Y (2020) Long short-term memory-based intrusion detection system for in-vehicle controller area network bus. In: 2020 IEEE 44th annual computers, software, and applications conference (COMPSAC), pp 10\u201317","DOI":"10.1109\/COMPSAC48688.2020.00011"},{"issue":"9","key":"195_CR13","doi-asserted-by":"publisher","first-page":"4332","DOI":"10.1109\/TNNLS.2021.3056664","volume":"33","author":"F Huang","year":"2022","unstructured":"Huang F, Li X, Yuan C, Zhang S, Zhang J, Qiao S (2022) Attention-emotion-enhanced convolutional lstm for sentiment analysis. IEEE Trans Neural Netw Learn Syst 33(9):4332\u20134345. https:\/\/doi.org\/10.1109\/TNNLS.2021.3056664","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"195_CR14","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1109\/TNSE.2021.3059881","volume":"8","author":"AR Javed","year":"2021","unstructured":"Javed AR, Rehman SU, Khan MU, Alazab M, Reddy TG (2021) Canintelliids: detecting in-vehicle intrusion attacks on a controller area network using cnn and attention-based gru. IEEE Trans Netw Sci Eng 8(2):1456\u20131466. https:\/\/doi.org\/10.1109\/TNSE.2021.3059881","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"195_CR15","doi-asserted-by":"publisher","unstructured":"Jin S, Chung J-G, Xu Y (2021) Signature-based intrusion detection system (ids) for in-vehicle can bus network. In: 2021 IEEE international symposium on circuits and systems (ISCAS), pp 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS51556.2021.9401087","DOI":"10.1109\/ISCAS51556.2021.9401087"},{"issue":"6","key":"195_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0155781","volume":"11","author":"M-J Kang","year":"2016","unstructured":"Kang M-J, Kang J-W (2016) Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6):1\u201317. https:\/\/doi.org\/10.1371\/journal.pone.0155781","journal-title":"PLoS ONE"},{"key":"195_CR17","doi-asserted-by":"publisher","first-page":"54979","DOI":"10.1109\/ACCESS.2020.2980523","volume":"8","author":"S Katragadda","year":"2020","unstructured":"Katragadda S, Darby PJ, Roche A, Gottumukkala R (2020) Detecting low-rate replay-based injection attacks on in-vehicle networks. IEEE Access 8:54979\u201354993. https:\/\/doi.org\/10.1109\/ACCESS.2020.2980523","journal-title":"IEEE Access"},{"key":"195_CR18","doi-asserted-by":"publisher","first-page":"102150","DOI":"10.1016\/j.cose.2020.102150","volume":"103","author":"K Kim","year":"2021","unstructured":"Kim K, Kim JS, Jeong S, Park J-H, Kim HK (2021) Cybersecurity for autonomous vehicles: review of attacks and defense. Comput Secur 103:102150. https:\/\/doi.org\/10.1016\/j.cose.2020.102150","journal-title":"Comput Secur"},{"key":"195_CR19","doi-asserted-by":"publisher","unstructured":"Kleberger P, Olovsson T, Jonsson E (2011) Security aspects of the in-vehicle network in the connected car. In: 2011 IEEE intelligent vehicles symposium (IV), pp 528\u2013533. https:\/\/doi.org\/10.1109\/IVS.2011.5940525","DOI":"10.1109\/IVS.2011.5940525"},{"key":"195_CR20","doi-asserted-by":"publisher","unstructured":"Larson UE, Nilsson DK (2008) Securing vehicles against cyber attacks. In: Proceedings of the 4th annual workshop on cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead. CSIIRW\u201908. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1413140.1413174","DOI":"10.1145\/1413140.1413174"},{"key":"195_CR21","doi-asserted-by":"publisher","unstructured":"Laszka A, Abbas W, Sastry SS, Vorobeychik Y, Koutsoukos X (2016) Optimal thresholds for intrusion detection systems. In: Proceedings of the symposium and bootcamp on the science of security. HotSos\u201916. Association for Computing Machinery, New York, NY, USA, pp 72\u201381. https:\/\/doi.org\/10.1145\/2898375.2898399","DOI":"10.1145\/2898375.2898399"},{"key":"195_CR22","doi-asserted-by":"publisher","first-page":"52139","DOI":"10.1109\/ACCESS.2022.3174356","volume":"10","author":"S Lee","year":"2022","unstructured":"Lee S, Jo HJ, Cho A, Lee DH, Choi W (2022) Ttids: Transmission-resuming time-based intrusion detection system for controller area network (can). IEEE Access 10:52139\u201352153. https:\/\/doi.org\/10.1109\/ACCESS.2022.3174356","journal-title":"IEEE Access"},{"key":"195_CR23","doi-asserted-by":"publisher","unstructured":"Lee H, Jeong SH, Kim HK (2017) Otids: a novel intrusion detection system for in-vehicle network by using remote frame. In: 2017 15th annual conference on privacy, security and trust (PST), vol. 00, pp 57\u20135709. https:\/\/doi.org\/10.1109\/PST.2017.00017","DOI":"10.1109\/PST.2017.00017"},{"key":"195_CR24","unstructured":"Li W (2004) Using genetic algorithm for network intrusion detection. In: Proceedings of the United States Department of Energy Cyber Security Group 2004 Training Conference, pp 24\u201327"},{"issue":"7","key":"195_CR25","doi-asserted-by":"publisher","first-page":"4579","DOI":"10.1109\/TITS.2020.3017183","volume":"22","author":"Z Lv","year":"2021","unstructured":"Lv Z, Lou R, Singh AK (2021) Ai empowered communication systems for intelligent transportation systems. IEEE Trans Intell Transp Syst 22(7):4579\u20134587. https:\/\/doi.org\/10.1109\/TITS.2020.3017183","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"195_CR26","doi-asserted-by":"publisher","unstructured":"Marchetti M, Stabili D (2017) Anomaly detection of can bus messages through analysis of id sequences. In: 2017 IEEE intelligent vehicles symposium (IV), pp 1577\u20131583. https:\/\/doi.org\/10.1109\/IVS.2017.7995934","DOI":"10.1109\/IVS.2017.7995934"},{"key":"195_CR27","unstructured":"Meyer S (2019) Vehicle hacking, the new data security threat. Accessed 17 Mar 2019. https:\/\/www.cpomagazine.com\/cyber-security\/vehicle-hacking-the-new-data-security-threat\/"},{"key":"195_CR28","unstructured":"Miller C, Valasek C (2015) Remote exploitation of an unaltered passenger vehicle. In: Proceedings of the Black Hat USA 2015"},{"key":"195_CR29","unstructured":"Miller C, Valasek C. Car hacking data. https:\/\/illmatics.com\/carhacking.html"},{"key":"195_CR30","doi-asserted-by":"publisher","unstructured":"Minawi O, Whelan J, Almehmadi A, El-Khatib K (2020) Machine learning-based intrusion detection system for controller area networks. In: Proceedings of the 10th ACM symposium on design and analysis of intelligent vehicular networks and applications. DIVANet\u201920. Association for Computing Machinery, New York, NY, USA, pp 41\u201347. https:\/\/doi.org\/10.1145\/3416014.3424581","DOI":"10.1145\/3416014.3424581"},{"key":"195_CR31","doi-asserted-by":"publisher","unstructured":"Mukkamala S, Sung AH (2003) Detecting denial of service attacks using support vector machines. In: The 12th IEEE international conference on fuzzy systems, 2003. FUZZ\u201903., vol. 2, pp 1231\u201312362. https:\/\/doi.org\/10.1109\/FUZZ.2003.1206607","DOI":"10.1109\/FUZZ.2003.1206607"},{"key":"195_CR32","doi-asserted-by":"publisher","first-page":"124931","DOI":"10.1109\/ACCESS.2021.3110524","volume":"9","author":"M Nam","year":"2021","unstructured":"Nam M, Park S, Kim DS (2021) Intrusion detection method using bi-directional gpt for in-vehicle controller area networks. IEEE Access 9:124931\u2013124944. https:\/\/doi.org\/10.1109\/ACCESS.2021.3110524","journal-title":"IEEE Access"},{"key":"195_CR33","unstructured":"NCCIC\/ICS-CERT: CAN Bus Standard Vulnerability (2017). Accessed 10 Aug 2018. https:\/\/www.cisa.gov\/news-events\/ics-alerts\/ics-alert-17-209-01"},{"issue":"4","key":"195_CR34","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/SURV.2008.080406","volume":"10","author":"TTT Nguyen","year":"2008","unstructured":"Nguyen TTT, Armitage G (2008) A survey of techniques for internet traffic classification using machine learning. IEEE Commun Surv Tutor 10(4):56\u201376. https:\/\/doi.org\/10.1109\/SURV.2008.080406","journal-title":"IEEE Commun Surv Tutor"},{"issue":"4","key":"195_CR35","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1109\/TNSE.2020.2990984","volume":"7","author":"L Nie","year":"2020","unstructured":"Nie L, Ning Z, Wang X, Hu X, Cheng J, Li Y (2020) Data-driven intrusion detection for intelligent internet of vehicles: a deep convolutional neural network-based method. IEEE Trans Netw Sci Eng 7(4):2219\u20132230. https:\/\/doi.org\/10.1109\/TNSE.2020.2990984","journal-title":"IEEE Trans Netw Sci Eng"},{"issue":"2","key":"195_CR36","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TVT.2019.2961344","volume":"69","author":"H Olufowobi","year":"2020","unstructured":"Olufowobi H, Young C, Zambreno J, Bloom G (2020) Saiducant: specification-based automotive intrusion detection using controller area network (can) timing. IEEE Trans Veh Technol 69(2):1484\u20131494. https:\/\/doi.org\/10.1109\/TVT.2019.2961344","journal-title":"IEEE Trans Veh Technol"},{"key":"195_CR37","doi-asserted-by":"publisher","first-page":"30069","DOI":"10.1109\/ACCESS.2022.3159339","volume":"10","author":"J Oruh","year":"2022","unstructured":"Oruh J, Viriri S, Adegun A (2022) Long short-term memory recurrent neural network for automatic speech recognition. IEEE Access 10:30069\u201330079. https:\/\/doi.org\/10.1109\/ACCESS.2022.3159339","journal-title":"IEEE Access"},{"issue":"6","key":"195_CR38","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1016\/j.patcog.2014.12.015","volume":"48","author":"S Peng","year":"2015","unstructured":"Peng S, Hu Q, Chen Y, Dang J (2015) Improved support vector machine algorithm for heterogeneous data. Pattern Recognit 48(6):2072\u20132083. https:\/\/doi.org\/10.1016\/j.patcog.2014.12.015","journal-title":"Pattern Recognit"},{"key":"195_CR39","doi-asserted-by":"publisher","unstructured":"Sekar R, Gupta A, Frullo J, Shanbhag T, Tiwari A, Yang H, Zhou S (2002) Specification-based anomaly detection: a new approach for detecting network intrusions. In: Proceedings of the 9th ACM conference on computer and communications security. CCS \u201902. ACM, New York, NY, USA, pp 265\u2013274. https:\/\/doi.org\/10.1145\/586110.586146","DOI":"10.1145\/586110.586146"},{"key":"195_CR40","doi-asserted-by":"publisher","unstructured":"Seo E, Song HM, Kim HK (2018) Gids: Gan based intrusion detection system for in-vehicle network. In: 2018 16th annual conference on privacy, security and trust (PST), pp 1\u20136. https:\/\/doi.org\/10.1109\/PST.2018.8514157","DOI":"10.1109\/PST.2018.8514157"},{"key":"195_CR41","doi-asserted-by":"publisher","unstructured":"Silveira\u00a0Barreto CA (2018) OBD-II datasets. Kaggle. https:\/\/doi.org\/10.34740\/KAGGLE\/DSV\/83155 . https:\/\/www.kaggle.com\/dsv\/83155","DOI":"10.34740\/KAGGLE\/DSV\/83155"},{"key":"195_CR42","doi-asserted-by":"publisher","unstructured":"Sung AH, Mukkamala S (2003) Identifying important features for intrusion detection using support vector machines and neural networks. In: 2003 Proceedings of the symposium on applications and the internet, pp 209\u2013216. https:\/\/doi.org\/10.1109\/SAINT.2003.1183050","DOI":"10.1109\/SAINT.2003.1183050"},{"issue":"1","key":"195_CR43","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MTS.2018.2795093","volume":"37","author":"Y Takefuji","year":"2018","unstructured":"Takefuji Y (2018) Connected vehicle security vulnerabilities [commentary]. IEEE Technol Soc Mag 37(1):15\u201318. https:\/\/doi.org\/10.1109\/MTS.2018.2795093","journal-title":"IEEE Technol Soc Mag"},{"key":"195_CR44","doi-asserted-by":"publisher","unstructured":"Tanksale V (2019) Intrusion detection for controller area network using support vector machines. In: 2019 IEEE 16th international conference on mobile ad hoc and sensor systems workshops (MASSW), pp 121\u2013126. https:\/\/doi.org\/10.1109\/MASSW.2019.00032","DOI":"10.1109\/MASSW.2019.00032"},{"key":"195_CR45","doi-asserted-by":"publisher","unstructured":"Tanksale V (2020a) Controller area network security requirements. In: 2020 International conference on computational science and computational intelligence (CSCI), pp 157\u2013162. https:\/\/doi.org\/10.1109\/CSCI51800.2020.00034","DOI":"10.1109\/CSCI51800.2020.00034"},{"key":"195_CR46","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1109\/OJITS.2020.3043066","volume":"1","author":"V Tanksale","year":"2020","unstructured":"Tanksale V (2020b) Anomaly detection for controller area networks using long short-term memory. IEEE Open J Intell Transp Syst 1:253\u2013265. https:\/\/doi.org\/10.1109\/OJITS.2020.3043066","journal-title":"IEEE Open J Intell Transp Syst"},{"key":"195_CR47","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/OJITS.2021.3104495","volume":"2","author":"V Tanksale","year":"2021","unstructured":"Tanksale V (2021) Design of anomaly detection functions for controller area networks. IEEE Open J Intell Transp Syst 2:312\u2013321. https:\/\/doi.org\/10.1109\/OJITS.2021.3104495","journal-title":"IEEE Open J Intell Transp Syst"},{"key":"195_CR48","doi-asserted-by":"publisher","unstructured":"Tanksale V (2023) Gated recurrent units for intrusion detection. In: 2023 IEEE IAS global conference on emerging technologies (GlobConET), pp 1\u20135. https:\/\/doi.org\/10.1109\/GlobConET56651.2023.10149912","DOI":"10.1109\/GlobConET56651.2023.10149912"},{"key":"195_CR49","doi-asserted-by":"publisher","unstructured":"Tavallaee M, Lu W, Iqbal SA, Ghorbani AA (2008) A novel covariance matrix based approach for detecting network anomalies. In: 6th annual communication networks and services research conference (cnsr 2008), pp 75\u201381. https:\/\/doi.org\/10.1109\/CNSR.2008.80","DOI":"10.1109\/CNSR.2008.80"},{"key":"195_CR50","doi-asserted-by":"crossref","unstructured":"Taylor A, Leblanc S, Japkowicz N (2016) Anomaly detection in automobile control network data with long short-term memory networks. In: 2016 IEEE international conference on data science and advanced analytics (DSAA), pp 130\u2013139","DOI":"10.1109\/DSAA.2016.20"},{"key":"195_CR51","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik V (2013) The nature of statistical learning theory. Springer, New York"},{"key":"195_CR52","unstructured":"Vector: CANoe. Vector. https:\/\/www.vector.com\/int\/en\/products\/products-a-z\/software\/canoe\/"},{"issue":"8","key":"195_CR53","doi-asserted-by":"publisher","first-page":"2248","DOI":"10.1109\/TITS.2016.2519464","volume":"17","author":"S Woo","year":"2016","unstructured":"Woo S, Jo HJ, Kim IS, Lee DH (2016) A practical security architecture for in-vehicle can-fd. IEEE Trans Intell Transp Syst 17(8):2248\u20132261. https:\/\/doi.org\/10.1109\/TITS.2016.2519464","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"195_CR54","doi-asserted-by":"publisher","first-page":"54607","DOI":"10.1109\/ACCESS.2018.2870695","volume":"6","author":"W Wu","year":"2018","unstructured":"Wu W, Kurachi R, Zeng G, Matsubara Y, Takada H, Li R, Li K (2018) Idh-can: a hardware-based id hopping can mechanism with enhanced security for automotive real-time applications. IEEE Access 6:54607\u201354623. https:\/\/doi.org\/10.1109\/ACCESS.2018.2870695","journal-title":"IEEE Access"},{"issue":"3","key":"195_CR55","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/TITS.2019.2908074","volume":"21","author":"W Wu","year":"2020","unstructured":"Wu W, Li R, Xie G, An J, Bai Y, Zhou J, Li K (2020) A survey of intrusion detection for in-vehicle networks. IEEE Trans Intell Transp Syst 21(3):919\u2013933. https:\/\/doi.org\/10.1109\/TITS.2019.2908074","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"195_CR56","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-030-35055-0_13","volume-title":"Secure IT systems","author":"W Xiong","year":"2019","unstructured":"Xiong W, G\u00fclsever M, Kaya KM, Lagerstr\u00f6m R (2019) A study of security vulnerabilities and software weaknesses in vehicles. In: Askarov A, Hansen RR, Rafnsson W (eds) Secure IT systems. Springer, Cham, pp 204\u2013218"},{"issue":"6","key":"195_CR57","doi-asserted-by":"publisher","first-page":"3362034","DOI":"10.1145\/3362034","volume":"18","author":"J Zhou","year":"2019","unstructured":"Zhou J, Joshi P, Zeng H, Li R (2019) Btmonitor: bit-time-based intrusion detection and attacker identification in controller area network. ACM Trans Embed Comput Syst 18(6):3362034. https:\/\/doi.org\/10.1145\/3362034","journal-title":"ACM Trans Embed Comput Syst"}],"container-title":["Cybersecurity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-023-00195-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42400-023-00195-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-023-00195-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T01:09:41Z","timestamp":1706836181000},"score":1,"resource":{"primary":{"URL":"https:\/\/cybersecurity.springeropen.com\/articles\/10.1186\/s42400-023-00195-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,2]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["195"],"URL":"https:\/\/doi.org\/10.1186\/s42400-023-00195-4","relation":{},"ISSN":["2523-3246"],"issn-type":[{"value":"2523-3246","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,2]]},"assertion":[{"value":"31 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not available.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval\u00a0and consent to participate"}},{"value":"Not available.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"None of the authors have any competing interests in the manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4"}}