Skip to main content
SpringerLink
Log in

Security Algorithm for Intelligent Transport System in Cyber-Physical Systems Perceptive: Attacks, Vulnerabilities, and Countermeasures

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Upcoming technological innovations and findings are immense in the field of intelligent transport systems (ITS). Cyber-physical systems (CPSs) are complex systems that integrate communication, control, and computing technology. CPSs are widely used today in intelligent transportation. For every development of a new system, there is a parallel offender who initiates the attack to destroy the root of the system developed. The attack category is infinite in such developing technology. In this paper, we have focused on preserving the security of ITS from cyber-physical systems perceptive, and various vulnerabilities, attacks, and countermeasures against ITS. Reinforcement learning is the latest buzzword in which an agent can understand and explain the environment, perform an action, and learn through trial and error. The paper also explains how reinforcement learning helps ITS in terms of security.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Belhadi A, Djenouri Y, Srivastava G, Lin JCW. SS-ITS: secure scalable intelligent transportation systems. J Supercomput. 2021;77(7):7253–69. https://doi.org/10.1007/s11227-020-03582-7.

    Article  Google Scholar 

  2. Pham M, Xiong K. A survey on security attacks and defense techniques for connected and autonomous vehicles. Comp Sec. 2021. https://doi.org/10.1016/j.cose.2021.102269.

    Article  Google Scholar 

  3. El-Rewini Z, Sadatsharan K, Selvaraj DF, Plathottam SJ, Ranganathan P. Cybersecurity challenges in vehicular communications. Vehicular Communicat. 2020. https://doi.org/10.1016/j.vehcom.2019.100214.

    Article  Google Scholar 

  4. Rasheed I, Hu F, Zhang L. Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN. Vehicular Communicat. 2020. https://doi.org/10.1016/j.vehcom.2020.100266.

    Article  Google Scholar 

  5. Sun X, Yu FR, Zhang P. A survey on cyber-security of connected and autonomous vehicles (CAVs). IEEE Trans Intell Transp Syst. 2022;23(7):6240–59. https://doi.org/10.1109/TITS.2021.3085297.

    Article  Google Scholar 

  6. El-Rewini Z, Sadatsharan K, Sugunaraj N, Selvaraj DF, Plathottam SJ, Ranganathan P. Cybersecurity attacks in vehicular sensors. IEEE Sens J. 2020;20(22):13752–67. https://doi.org/10.1109/JSEN.2020.3004275.

    Article  Google Scholar 

  7. Tlili F, Fourati LC, Ayed S, Ouni B. Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: assessments & countermeasures. Ad Hoc Net. 2022. https://doi.org/10.1016/j.adhoc.2022.102805.

    Article  Google Scholar 

  8. Asghar Khan M, et al. A provable and privacy-preserving authentication scheme for UAV-enabled intelligent transportation systems. IEEE Trans Industr Inform. 2022;18(5):3416–25. https://doi.org/10.1109/TII.2021.3101651.

    Article  Google Scholar 

  9. Kong PY. A survey of cyberattack countermeasures for unmanned aerial vehicles. IEEE Access. 2021;9:148244–63. https://doi.org/10.1109/ACCESS.2021.3124996.

    Article  Google Scholar 

  10. Chowdhury A, Karmakar G, Kamruzzaman J, Islam S. Trustworthiness of self-driving vehicles for intelligent transportation systems in industry applications. IEEE Trans Industr Inform. 2021;17(2):961–70. https://doi.org/10.1109/TII.2020.2987431.

    Article  Google Scholar 

  11. Fei F, Tu Z, Xu D, Deng X (2020) Learn-to-Recover: Retrofitting UAVs with Reinforcement Learning-Assisted Flight Control Under Cyber-Physical Attacks, Learn-to-Recover: Retrofitting UAVs with Reinforcement Learning-Assisted Flight Control Under Cyber-Physical Attacks.

  12. Guo RX, Tian JW, Wang BH, te Shang F (2020) “Cyber-Physical Attack Threats Analysis for UAVs from CPS Perspective.” In: Proceedings—2020 International Conference on Computer Engineering and Application, ICCEA 2020, p. 259–263. doi: https://doi.org/10.1109/ICCEA50009.2020.00063.

  13. Cao L, Jiang X, Zhao Y, Wang S, You D, Xu X. A survey of network attacks on cyber-physical systems. IEEE Access. 2020;8:44219–27. https://doi.org/10.1109/ACCESS.2020.2977423.

    Article  Google Scholar 

  14. Chowdhury A, Karmakar G, Kamruzzaman J, Jolfaei A, Das R. Attacks on self-driving cars and their countermeasures: a survey. IEEE Access. 2020;8:207308–42. https://doi.org/10.1109/ACCESS.2020.3037705.

    Article  Google Scholar 

  15. Sakthivel S. UBP-trust: user behavioral pattern based secure trust model for mitigating denial of service attacks in software as a service (SaaS) cloud environment. J Computat Theoret Nanosci. 2016;13(10):7649.

    Article  Google Scholar 

  16. Ribouh S, Phan K, Malawade AV, Elhillali Y, Rivenq A, Al Faruque MA. Channel state information-based cryptographic key generation for intelligent transportation systems. IEEE Transact Intell Transportat Sys. 2021;22(12):7496–507. https://doi.org/10.1109/TITS.2020.3003577.

    Article  Google Scholar 

  17. Hammar K, Stadler R (2020) “Finding Effective Security Strategies through Reinforcement Learning and Self-Play,” In: 2020 16th International Conference on Network and Service Management (CNSM), doi: https://doi.org/10.23919/CNSM50824.2020.9269092.

  18. Zhang J, Pan L, Han QL, Chen C, Wen S, Xiang Y. Deep learning based attack detection for cyber-physical system cybersecurity: a survey. IEEE/CAA Journal of Automatica Sinica. 2022;9(3):377–91. https://doi.org/10.1109/JAS.2021.1004261.

    Article  Google Scholar 

  19. Ahmed Jamal A, Mustafa Majid A-A, Konev A, Kosachenko T, Shelupanov A. A review on security analysis of cyber physical systems using machine learning. Mater Today Proc. 2021. https://doi.org/10.1016/j.matpr.2021.06.320.

    Article  Google Scholar 

  20. Cai X, Han K, Li Y, Wang H, Zhang J, Zhang Y (2020) “Research on Security Estimation and Control of Cyber-Physical System,” doi: https://doi.org/10.1109/IPCCC50635.2020.9391573.

  21. Karthick K, et al. Iterative dichotomiser posteriori method-based service attack detection in cloud computing. Comput Syst Sci Eng. 2023;44(2):1099–107.

    Article  Google Scholar 

  22. Dong C, Wang H, Ni D, Liu Y, Chen Q. Impact evaluation of cyber-attacks on traffic flow of connected and automated vehicles. IEEE Access. 2020;8:86824–35. https://doi.org/10.1109/ACCESS.2020.2993254.

    Article  Google Scholar 

  23. Kiran BR, et al. Deep reinforcement learning for autonomous driving: a survey. IEEE Trans Intell Transp Syst. 2022;23(6):4909–26. https://doi.org/10.1109/TITS.2021.3054625.

    Article  Google Scholar 

  24. Haydari A, Yilmaz Y. Deep reinforcement learning for intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst. 2022;23(1):11–32. https://doi.org/10.1109/TITS.2020.3008612.

    Article  Google Scholar 

  25. Bhattacharya A, Ramachandran T, Banik S, Dowling CP, Bopardikar SD (2020) “Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning,” doi: https://doi.org/10.1109/ISI49825.2020.9280521.

  26. https://towardsdatascience.com/applying-of-reinforcement-learning-for-self-driving-cars-8fd87b255b81

Download references

Author information

Authors and Affiliations

  1. Dr.N.G.P. Institute of Technology, Coimbatore, India

    R. Pavithra

  2. KPR Institute of Engineering and Technology, Coimbatore, India

    Vishnu kumar Kaliappan & Sivaramakrishnan Rajendar

Authors
  1. R. Pavithra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vishnu kumar Kaliappan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sivaramakrishnan Rajendar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vishnu kumar Kaliappan.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Industrial IoT and Cyber-Physical Systems” guest edited by Arun K Somani, Seeram Ramakrishnan, Anil Chaudhary and Mehul Mahrishi.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pavithra, R., Kaliappan, V.k. & Rajendar, S. Security Algorithm for Intelligent Transport System in Cyber-Physical Systems Perceptive: Attacks, Vulnerabilities, and Countermeasures. SN COMPUT. SCI. 4, 544 (2023). https://doi.org/10.1007/s42979-023-01897-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-01897-9

Keywords

Search

Navigation