Designing of New Reliable Control Architecture for ConnectedAutonomous Vehicles Against Cyber ‎Attacks

  • Authors

    • Shwetansh Priyadarshi Research Scholar ‎ GD Goenka University, Gurugram
    • Dr. Deepak Bharadwaj Associate Professor‎ DBS Global University, Dehradun
    https://doi.org/10.14419/x1ejje10

    Received date: July 8, 2025

    Accepted date: August 10, 2025

    Published date: August 19, 2025

  • Autonomous Vehicles; Connected Vehicles; Cyber-Attacks; Cyber-Crimes

  • Abstract

    Autonomous vehicles were introduced with the idea that they would make jobs easy, it will help in ‎reducing accident rates, reduce pollutants, lower harmful gas emissions, and lighter ‎traffic congestion, etc. Connected vehicles use a variety of devices, sensors, cameras, and ‎modules like LiDAR, GPS, RADAR, onboard computers, ultrasonic sensors, etc., to ‎make proper driving decisions and to be competent to work on the road. In recent articles on ‎cyber-attacks/cyber-crimes/accidents, data states that hackers and other ill-motive ‎organisations can do remote hacking, tamper with the sensor data, and they may crash ‎a vehicle or access the primary control by attack, which may result in significant losses. We ‎have observed in the past few years that Autonomous vehicle makers and the administration are ‎refraining from investing in completely Automated vehicles. The interest rate of ‎people in connected AVs has gradually fallen with each passing year, and many surveys ‎clearly mentioned that the main reason for this is the lack of a strong security framework. This paper ‎provides a brief about cyber-attacks on AVs and different approaches to deal with them‎.

  • References

    1. Anastasios, G., Aristeidis, K. (2023). Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions
    2. Shaoshan, L., Liyun, L., Jie, Tang. Creating Autonomous Vehicles Systems
    3. Miller, C., & Valasek, C. (2015). Remote exploitation of an unaltered passenger vehicle. Black Hat USA, 2015, 91
    4. CAV, Cav mounted devices, “ https://ecotrons.com/news/introduction-to-autonomous-driving-sensors-blog/”, online web source
    5. Z. Ju, H. Zhang, X. Li, X. Chen, J. Han, and M. Yang, “A survey on attack detection and resilience for connected and automated vehicles: From ve-hicle dynamics and control perspective,” IEEE Transactions on Intelligent Vehicles, 2022 https://doi.org/10.1109/TIV.2022.3186897.
    6. T. Limbasiya, K. Z. Teng, S. Chattopadhyay, and J. Zhou, “A systematic survey of attack detection and prevention in connected and autonomous ve-hicles,” Vehicular Communications, p. 100515, 2022. https://doi.org/10.1016/j.vehcom.2022.100515.
    7. M.Dibaei, X. Zheng, K. Jiang, R. Abbas, S. Liu, Y. Zhang, Y. Xiang, and S. Yu, “Attacks and defences on intelligent connected vehicles: a survey,” Digital Communications and Networks, vol. 6, no. 4, pp. 399–421, 2020. https://doi.org/10.1016/j.dcan.2020.04.007.
    8. E. Aliwa, O. Rana, C. Perera, and P. Burnap, “Cyberattacks and counter measures for in-vehicle networks,” ACM Computing Surveys (CSUR), vol. 54, no. 1, pp. 1–37, 2021. https://doi.org/10.1145/3431233.
    9. L. Liu, C. Chen, Q. Pei, S. Maharjan, and Y. Zhang, “Vehicular edge computing and networking: A survey,” Mobile Networks and Applications, vol. 26, no. 3, pp. 1145–1168, 2021. https://doi.org/10.1007/s11036-020-01624-1.
    10. X. Sun, F. R. Yu, and P. Zhang, “A survey on cyber-security of connected and autonomous vehicles (cavs),” IEEE Transactions on Intelli gent Trans-portation Systems, 2021. https://doi.org/10.1109/TITS.2021.3085297.
    11. Statista, “System architecture for Autonomous Vehicles.” https://encyclopedia.pub/entry/8473, online web source.
    12. M. Pham and K. Xiong, “A survey on security attacks and defense tech niques for connected and autonomous vehicles,” Computers & Security, p. 102269, 2021. https://doi.org/10.1016/j.cose.2021.102269.
    13. Statista, “Components which can be attacked in CAVs.” https://www.researchgate.net/figure/Cybersecurity-attack-vectors-in-CAVs_fig3_374030057, online web source.
    14. Y. Li, Q. Luo, J. Liu, H. Guo, and N. Kato, “Tsp security in intelligent and connected vehicles: Challenges and solutions,” IEEE Wireless Communi-cations, vol. 26, no. 3, pp. 125–131, 2019. https://doi.org/10.1109/MWC.2019.1800289.
    15. C. Maple, M. Bradbury, A. T. Le, and K. Ghirardello, “A connected and autonomous vehicle reference architecture for attack surface analysis,” Ap-plied Sciences, vol. 9, no. 23, p. 5101, 2019. https://doi.org/10.3390/app9235101.
    16. I. Butun, P. Österberg, and H. Song, “Security of the internet of things: Vulnerabilities, attacks, and countermeasures,” IEEE Communications Sur-veys & Tutorials, vol. 22, no. 1, pp. 616–644, 2019. https://doi.org/10.1109/COMST.2019.2953364.
    17. Z. Ju, H. Zhang, X. Li, X. Chen, J. Han, and M. Yang, “A survey on attack detection and resilience for connected and automated vehicles: From ve-hicle dynamics and control perspective,” IEEE Transactions on Intelligent Vehicles, 2022. https://doi.org/10.1109/TIV.2022.3186897.
    18. T. Limbasiya, K. Z. Teng, S. Chattopadhyay, and J. Zhou, “A systematic survey of attack detection and prevention in connected and autonomous ve-hicles,” Vehicular Communications, p. 100515, 2022. https://doi.org/10.1016/j.vehcom.2022.100515.
    19. E. Aliwa, O. Rana, C. Perera, and P. Burnap, “Cyberattacks and counter measures for in-vehicle networks,” ACM Computing Surveys (CSUR), vol. 54, no. 1, pp. 1–37, 2021. https://doi.org/10.1145/3431233.
    20. K. Kim, J. S. Kim, S. Jeong, J.-H. Park, and H. K. Kim, “Cybersecurity for autonomous vehicles: Review of attacks and defense,” Computers & Se-curity, p. 102150, 2021. https://doi.org/10.1016/j.cose.2020.102150.
    21. D. K. Hong, J. Kloosterman, Y. Jin, Y. Cao, Q. A. Chen, S. Mahlke, and Z. M. Mao, “Avguardian: Detecting and mitigating publish-subscribe over-privilege for autonomous vehicle systems,” in Proceedings of the European Symposium on Security and Privacy (Euro S&P), pp. 445–459, IEEE, 2020. https://doi.org/10.1109/EuroSP48549.2020.00035.
    22. P. Xiong, S. Buffett, S. Iqbal, P. Lamontagne, M. Mamun, and H. Molyneaux, “Towards a robust and trustworthy machine learning system develop-ment: An engineering perspective,” Journal of Information Security and Applications, vol. 65, p. 103121, 2022. https://doi.org/10.1016/j.jisa.2022.103121.
    23. T. Lauser, D. Zelle, and C. Krauß, “Security analysis of automotive protocols,” in Proceedings of the Computer Science in Cars Symposium, pp. 1–12, 2020. https://doi.org/10.1145/3385958.3430482.
    24. A. S. Thangarajan, M. Ammar, B. Crispo, and D. Hughes, “Towards bridging the gap between modern and legacy automotive ecus: A software-based security framework for legacy ecus,” in Proceedings of the 2nd Connected and Automated Vehicles Symposium (CAVS), pp. 1 5, IEEE, 2019. https://doi.org/10.1109/CAVS.2019.8887788.
    25. A. K. Malhi, S. Batra, and H. S. Pannu, “Security of vehicular ad-hoc networks: A comprehensive survey,” Computers & Security, vol. 89, p. 101664, 2020. https://doi.org/10.1016/j.cose.2019.101664.
    26. V. Desnitsky, N. Rudavin, and I. Kotenko, “Modeling and evaluation of battery depletion attacks on unmanned aerial vehicles in crisis man agement systems,” in Proceedings of the International Symposium on Intelligent and Distributed Computing, pp. 323–332, Springer, 2019. https://doi.org/10.1007/978-3-030-32258-8_38.
    27. V. L. Thing and J. Wu, “Autonomous vehicle security: A taxonomy of attacks and defences,” in Proceedings of the international conference on inter-net of things (ithings) and ieee green computing and communications (greencom) and ieee cyber, physical and social computing (cpscom) and ieee smart data (smartdata), pp. 164–170, IEEE, 2016. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.52.
    28. F. Van Wyk, Y. Wang, A. Khojandi, and N. Masoud, “Real-time sensor anomaly detection and identification in automated vehicles,” IEEE Trans ac-tions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1264 1276, 2019. https://doi.org/10.1109/TITS.2019.2906038.
    29. D. Wei and X. Qiu, “Status-based detection of malicious code in internet of things (iot) devices,” in Proceedings of the IEEE Conference on Commu-nications and Network Security (CNS), pp. 1–7, IEEE, 2018. https://doi.org/10.1109/CNS.2018.8433183.
    30. S. Jeong, B. Jeon, B. Chung, and H. K. Kim, “Convolutional neural network-based intrusion detection system for avtp streams in auto motive ether-net-based networks,” Vehicular Communications, vol. 29, p. 100338, 2021. https://doi.org/10.1016/j.vehcom.2021.100338.
    31. T. Shu and M. Krunz, “Privacy-preserving and truthful detection of packet dropping attacks in wireless ad hoc networks,” IEEE Transactions on mo-bile computing, vol. 14, no. 4, pp. 813–828, 2014. https://doi.org/10.1109/TMC.2014.2330818.
    32. Statista “how attack on one CAV can impact other CAVs and complete Infrastructure.” https://www.researchgate.net/figure/Cyber-attack-vectors-in-CAVs-Vehicle-to-Everything-V2X-Communication-Various-wireless_fig8_344947562, online web source.
    33. Miller, C.; Valasek, C. Remote Exploitation of an Unaltered Passenger Vehicle. Black Hat USA 2015, 2015, 1–91.
    34. Nie, S.; Liu, L.; Du, Y. Free-Fall: Hacking Tesla from Wireless to CAN Bus. Keen Security Lab in Black Hat USA. 2017. Available online: free-fall-hacking-tesla-from-wireless-to-can-bus (accessed on 20 March 2024).
    35. Cai, Z.; Wang, A.; Zhang, W.; Gruffke, M.; Schweppe, H. Roadways to Exploit and Secure Connected BMW Cars. Keen Security Lab in Black Hat USA. 2019. Available online: Microsoft Word - 0-Days&Mitigations-Roadways-to-Exploit-and-Secure-Connected-BMW-Cars.docx (accessed on 20 March 2024).
    36. R. Passerone, D. Cancila, M. Albano, S. Mouelhi, S. Plosz, E. Jantunen, A. Ryabokon, E. Laarouchi, C. Heged˝ us, and P. Varga, “A methodol ogy for the design of safety-compliant and secure communication of autonomous vehicles,” IEEE Access, vol. 7, pp. 125022–125037, 2019. https://doi.org/10.1109/ACCESS.2019.2937453.
    37. S. Peter and T. Givargis, “Towards a timing attack aware high-level synthesis of integrated circuits,” in Proceedings of the 34th International Confer-ence on Computer Design (ICCD), pp. 452–455, IEEE, 2016. https://doi.org/10.1109/ICCD.2016.7753326.
    38. L. Li, J. Sun, Y. Liu, M. Sun, and J.-S. Dong, “A formal specification and verification framework for timed security protocols,” IEEE Transactions on Software Engineering, vol. 44, no. 8, pp. 725–746, 2017. https://doi.org/10.1109/TSE.2017.2712621.
    39. Y. Sasaki, T. Emaru, and A. A. Ravankar, “SVM based pedestrian detection system for sidewalk snow removing machines,” in Proc. IEEE/SICE Int. Symp. Syst. Integration, 2021, pp. 700–701. https://doi.org/10.1109/IEEECONF49454.2021.9382618.
    40. Y.Bian,J.Ding,M.Hu,Q.Xu,J.Wang,andK.Li,“Anadvancedlane keeping assistance system with switchable assistance modes,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 1, pp. 385–396, Jan. 2020. https://doi.org/10.1109/TITS.2019.2892533.
    41. Chowdhury, Muktadir, Ashlesh Gawande, and Lan Wang. "Secure information sharing among autonomous vehicles in NDN." 2017 IEEE/ACM Second International Conference on Internet-of Things Design and Implementation (IoTDI). 2017 https://doi.org/10.1145/3054977.3054994.
    42. Shin, H., Kim, D., Kwon, Y., & Kim, Y. (2017, September). Illusion and dazzle: Adversarial optical channel exploits against lidars for automotive ap-plications. In International Conference on Cryptographic Hardware and Embedded Systems (pp. 445-467). Springer, Cham. https://doi.org/10.1007/978-3-319-66787-4_22.
    43. Litman, Todd. Autonomous vehicle implementation predictions. Victoria, Canada: Victoria Transport Policy Institute, 2017.
    44. B. Sheehan, F. Murphy, M. Mullins, and C. Ryan, “Connected and autonomous vehicles: A cyber-risk classification framework,” Transp. Res. Part A Policy Pract., vol. 124, no. November 2018, pp. 523–536, 2019, https://doi.org/10.1016/j.tra.2018.06.033.
    45. M. F. Zolkipli and A. Jantan, “Malware behavior analysis: Learning and understanding current malware threats,” Proc. - 2nd Int. Conf. Netw. Appl. Protoc. Serv. NETAPPS 2010, no. September, pp. 218–221, https://doi.org/10.1109/NETAPPS.2010.46.
    46. Lee, H.; Jeong, S.H.; Kim, H.K. OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame. In Proceedings of the 2017 15th Annual Conference on Privacy, Security and Trust (PST), Calgary, AB, Canada, 28–30 August 2017; pp. 57–5709. https://doi.org/10.1109/PST.2017.00017.
    47. Lo,W.; Alqahtani, H.; Thakur, K.; Almadhor, A.; Chander, S.; Kumar, G. A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic. Veh. Commun. 2022, 35, 100471. https://doi.org/10.1016/j.vehcom.2022.100471.
    48. GuardKnox. Zonal Architecture: The Foundation for Next Generation Vehicles. Available online: https://learn.guardknox.com/ zonal-architecturethe-foundation-for-next-generation-vehicles-1 (accessed on 5 August 2023).
    49. Lo,W.; Alqahtani, H.; Thakur, K.; Almadhor, A.; Chander, S.; Kumar, G. A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic. Veh. Commun. 2022, 35, 100471. https://doi.org/10.1016/j.vehcom.2022.100471.
    50. Eckermann,E.WorldHistoryoftheAutomobile;SAEPress:Warrendale,PA,USA,2001.
    51. Zhang,T.;Antunes,H.;Aggarwal,S.DefendingConnectedVehiclesAgainstMalware:ChallengesandaSolutionFramework. IEEEInter-netThingsJ.2014,1,10–21. https://doi.org/10.1109/JIOT.2014.2302386.

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    Priyadarshi, S., & Bharadwaj , D. D. . (2025). Designing of New Reliable Control Architecture for ConnectedAutonomous Vehicles Against Cyber ‎Attacks. International Journal of Basic and Applied Sciences, 14(4), 515-521. https://doi.org/10.14419/x1ejje10