Advancing Vehicle Security: Deep Learning based Solution for Defending CAN Networks in the Internet of Vehicles
DOI:
https://doi.org/10.4108/eetiot.6523Keywords:
Autonomous vehicle, Internet of Vehicles (IoV), Controller Area Network (CAN), cybersecurity, deep learning, vehicular communication systemsAbstract
The Internet of Vehicle (IoV) is revolutionizing the automobile sector by allowing vehicles to interact with one another and with roadside infrastructure. The Controller Area Network (CAN) is a vital component of such smart vehicles, allowing communication between various Electronic Control Units (ECUs). However, the CAN protocol's intrinsic lack of security renders it open to a variety of cyber-attacks, posing substantial hazards to both safety and privacy.
This research investigates the use of deep learning with multi-layer perceptron to improve the security of CAN networks inside the IoV framework. We discuss current threats to CAN networks, including spoofing, replay, and denial-of-service attacks, and how deep learning may be used to identify and mitigate these threats efficiently. We propose a unique deep learning-based defense mechanism for real-time threat detection.
The suggested method is highly effective in identifying and mitigating potential risks, as evidenced by extensive testing on real-world CAN datasets. Based on our findings, the proposed solution has the potential to considerably enhance the security of CAN networks in the Internet of Vehicles, making car communication systems more secure and reliable.
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[1] R. Islam and R. U. D. Refat, “Improving CAN bus security by assigning dynamic arbitration IDs,” J. Transp. Secur., vol. 13, no. 1–2, pp. 19–31, Jun. 2020, doi: 10.1007/s12198-020-00208-0.[2]O.-R. A. D. (ORAD) Committee, Taxonomy anddefinitions for terms related to driving automationsystems for on-road motor vehicles. SAE international,2021.
[3] P. Gershon, S. Seaman, B. Mehler, B. Reimer, and J.Coughlin, “Driver behavior and the use of automationin real-world driving,” Accid. Anal. Prev., vol. 158, p.106217, Aug. 2021, doi: 10.1016/j.aap.2021.106217.
[4] N. Sharma, N. Chauhan, and N. Chand, “Securitychallenges in Internet of Vehicles (IoV) environment,”in 2018 First International Conference on SecureCyber Computing and Communication (ICSCCC),Jalandhar, India: IEEE, Dec. 2018, pp. 203–207. doi:10.1109/ICSCCC.2018.8703272.
[5] K. Aswal, D. C. Dobhal, and H. Pathak, “Comparativeanalysis of machine learning algorithms foridentification of BOT attack on the Internet of Vehicles(IoV),” in 2020 International Conference on InventiveComputation Technologies (ICICT), IEEE, Feb. 2020,pp. 312–317. doi:10.1109/ICICT48043.2020.9112422.
[6] Q. Zhao, M. Chen, Z. Gu, S. Luan, H. Zeng, and S.Chakrabory, “CAN Bus Intrusion Detection Based onAuxiliary Classifier GAN and Out-of-distributionDetection,” ACM Trans. Embed. Comput. Syst., vol.21, no. 4, pp. 1–30, Jul. 2022, doi: 10.1145/3540198.
[7] H. Sun et al., “CCID-CAN: Cross-Chain IntrusionDetection on CAN Bus for Autonomous Vehicles,”IEEE Internet Things J., pp. 1–1, 2024, doi:10.1109/JIOT.2024.3393122.
[8] E. Aliwa, O. Rana, C. Perera, and P. Burnap,“Cyberattacks and Countermeasures for In-VehicleNetworks,” ACM Comput. Surv., vol. 54, no. 1, pp. 1–37, Jan. 2022, doi: 10.1145/3431233.
[9] T. Zhang, H. Antunes, and S. Aggarwal, “Defendingconnected vehicles against malware: Challenges and asolution framework,” IEEE Internet Things J., vol. 1,no. 1, pp. 10–21, 2014, doi:10.1109/JIOT.2014.2302386.
[10] S.-H. Chen and C.-H. R. Lin, “Evaluation of DoSAttacks on Vehicle CAN Bus System,” in RecentAdvances in Intelligent Information Hiding andMultimedia Signal Processing, vol. 110, J.-S. Pan, A.Ito, P.-W. Tsai, and L. C. Jain, Eds., in SmartInnovation, Systems and Technologies, vol. 110. ,Cham: Springer International Publishing, 2019, pp.308–314. doi: 10.1007/978-3-030-03748-2_38.
[11] H. J. Jo and W. Choi, “A Survey of Attacks onController Area Networks and CorrespondingCountermeasures,” IEEE Trans. Intell. Transp. Syst.,vol. 23, no. 7, pp. 6123–6141, Jul. 2022, doi:10.1109/TITS.2021.3078740.
[12] M. Hanselmann, T. Strauss, K. Dormann, and H.Ulmer, “CANet: An Unsupervised Intrusion DetectionSystem for High Dimensional CAN Bus Data,” IEEEAccess, vol. 8, pp. 58194–58205, 2020, doi:10.1109/ACCESS.2020.2982544.
[13] J. Liu, S. Zhang, W. Sun, and Y. Shi, “In-VehicleNetwork Attacks and Countermeasures: Challengesand Future Directions,” IEEE Netw., vol. 31, no. 5, pp.50–58, 2017, doi: 10.1109/MNET.2017.1600257.
[14] M. Bozdal, M. Samie, S. Aslam, and I. Jennions,“Evaluation of CAN Bus Security Challenges,”Sensors, vol. 20, no. 8, p. 2364, Apr. 2020, doi:10.3390/s20082364.
[15] T. Eisenbarth, T. Kasper, A. Moradi, C. Paar, M.Salmasizadeh, and M. T. M. Shalmani, “On the powerof power analysis in the real world: A complete breakof the KeeLoq code hopping scheme,” in Advances inCryptology–CRYPTO 2008: 28th AnnualInternational Cryptology Conference, Santa Barbara,CA, USA, August 17-21, 2008. Proceedings 28,Springer, 2008, pp. 203–220.
[16] K. Koscher et al., “Experimental security analysis of amodern automobile,” in 2010 IEEE symposium onsecurity and privacy, IEEE, 2010, pp. 447–462.
[17] C. Miller and C. Valasek, “Remote exploitation of anunaltered passenger vehicle,” Black Hat USA, vol.2015, no. S 91, pp. 1–91, 2015.
[18] J. Petit, B. Stottelaar, M. Feiri, and F. Kargl, “Remoteattacks on automated vehicles sensors: Experiments oncamera and lidar,” Black Hat Eur., vol. 11, no. 2015, p.995, 2015.
[19] Z. Zorz, “Backdooring connected cars for covertremote control—Help Net Security (2018),” RetrievedAugust, 2020.
[20] A. Palanca, E. Evenchick, F. Maggi, and S. Zanero, “Astealth, selective, link-layer denial-of-service attack against automotive networks,” in Detection of Intrusions and Malware, and Vulnerability Assessment: 14th International Conference, DIMVA 2017, Bonn, Germany, July 6-7, 2017, Proceedings 14, Springer, 2017, pp. 185–206.
[21] S. Woo, H. J. Jo, and D. H. Lee, “A practical wirelessattack on the connected car and security protocol forin-vehicle CAN,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 2, pp. 993–1006, 2014.
[22] S. Nie, L. Liu, and Y. Du, “Free-fall: hacking teslafrom wireless to can bus,” Defcon, pp. 1–16, 2017.
[23] S. Mukherjee, H. Shirazi, I. Ray, J. Daily, and R.Gamble, “Practical DoS attacks on embeddednetworks in commercial vehicles,” in InformationSystems Security: 12th International Conference,ICISS 2016, Jaipur, India, December 16-20, 2016,Proceedings 12, Springer, 2016, pp. 23–42.
[24] I. Ahmed, G. Jeon, and A. Ahmad, “Deep Learning-Based Intrusion Detection System for Internet ofVehicles,” IEEE Consum. Electron. Mag., vol. 12, no.1, pp. 117–123, Jan. 2023, doi:10.1109/MCE.2021.3139170.
[25] T. Alladi, V. Kohli, V. Chamola, and F. R. Yu, “A deeplearning based misbehavior classification scheme forintrusion detection in cooperative intelligenttransportation systems,” Digit. Commun. Netw., vol. 9,no. 5, pp. 1113–1122, Oct. 2023, doi:10.1016/j.dcan.2022.06.018.
[26] M. D. Hossain, H. Inoue, H. Ochiai, D. Fall, and Y.Kadobayashi, “LSTM-Based Intrusion DetectionSystem for In-Vehicle Can Bus Communications,”IEEE Access, vol. 8, pp. 185489–185502, 2020, doi:10.1109/ACCESS.2020.3029307.
[27] H. M. Song, J. Woo, and H. K. Kim, “In-vehiclenetwork intrusion detection using deep convolutionalneural network,” Veh. Commun., vol. 21, p. 100198,Jan. 2020, doi: 10.1016/j.vehcom.2019.100198.
[28] J. Zhang, F. Li, H. Zhang, R. Li, and Y. Li, “Intrusiondetection system using deep learning for in-vehiclesecurity,” Ad Hoc Netw., vol. 95, p. 101974, Dec.2019, doi: 10.1016/j.adhoc.2019.101974.
[29] Zhou, Li, and Shen, “Anomaly Detection of CAN BusMessages Using A Deep Neural Network forAutonomous Vehicles,” Appl. Sci., vol. 9, no. 15, p.3174, Aug. 2019, doi: 10.3390/app9153174.
[30] P. Mansourian, N. Zhang, A. Jaekel, and M. Kneppers,“Deep Learning-Based Anomaly Detection forConnected Autonomous Vehicles UsingSpatiotemporal Information,” IEEE Trans. Intell.Transp. Syst., vol. 24, no. 12, pp. 16006–16017, Dec.2023, doi: 10.1109/TITS.2023.3286611.
[31] E. C. P. Neto et al., “CICIoV2024: Advancing realisticIDS approaches against DoS and spoofing attack inIoV CAN bus,” Internet Things, vol. 26, p. 101209, Jul.2024, doi: 10.1016/j.iot.2024.101209.
[32] E. C. P. Neto, H. Taslimasa, S. Dadkhah, S. Iqbal, P.Xiong, T. Rahmanb, and A. A. Ghorbani, “CIC IoVdataset 2024, Canadian Institute for Cybersecurity,https://www.unb.ca/cic/datasets/iov-dataset-2024.html.” 2024. [Online]. Available:https://www.unb.ca/cic/datasets/iov-dataset-2024.html
[33] Sudhakar and S. Kumar, “An emerging threat Filelessmalware: a survey and research challenges,”Cybersecurity, vol. 3, no. 1, p. 1, Dec. 2020, doi:10.1186/s42400-019-0043-x.
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