CSPM EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 2 (2023) Security and Safety, 2 (2023) 2023027 References
Issue
Security and Safety
Volume 2, 2023
Security and Safety in Unmanned Systems
Article Number 2023027
Number of page(s) 19
Section Intelligent Transportation
DOI https://doi.org/10.1051/sands/2023027
Published online 16 October 2023
  1. Naufal JK, Camargo JB and Vismari LF et al. A2CPS: A vehicle-centric safety conceptual framework for autonomous transport systems. IEEE Trans Intell Transp Syst 2017; 19: 1925–39. [Google Scholar]
  2. Colabianchi S, Costantino F and Di Gravio G et al. Discussing resilience in the context of cyber physical systems. Comput Ind Eng 2021; 160: 107534. [CrossRef] [Google Scholar]
  3. Pajic M, Weimer J and Bezzo N et al. Design and implementation of attack-resilient cyberphysical systems: With a focus on attack-resilient state estimators. IEEE Trans Contr Syst Mag 2017; 37: 66–81. [CrossRef] [Google Scholar]
  4. Ye J, Guo LL and Yang B et al. Cyber-physical security of powertrain systems in modern electric vehicles: Vulnerabilities, challenges, and future visions. IEEE J Emerg Sel Top Power Electron 2021; 9: 4639–57. [CrossRef] [Google Scholar]
  5. Kim K, Kim JS and Jeong S et al. Cybersecurity for autonomous vehicles: Review of attacks and defense. Comput Secur 2021; 103: 102150. [CrossRef] [Google Scholar]
  6. Shoukry Y, Martin P and Tabuada P et al. Non-invasive spoofing attacks for anti-lock braking systems. In: International Conference on Cryptographic Hardware and Embedded Systems (CHES 2013), 2013, 55–72. [Google Scholar]
  7. Tippenhauer NO, Pöopper C and Rasmussen KB et al. On the requirements for successful GPS spoofing attacks. In: Proceedings of the 18th ACM Conference on Computer and Communications Security, 2011, 75–86. [Google Scholar]
  8. Koscher K, Czeskis A and Roesner F et al. Experimental security analysis of a modern automobile. In: 2010 IEEE Symposium on Security and Privacy. IEEE, 2010, 447–62. [CrossRef] [Google Scholar]
  9. Checkoway S, McCoy D and Kantor B et al. Comprehensive experimental analyses of automotive attack surfaces. In: USENIX Security Symposium 2011, 2021 447–62. [Google Scholar]
  10. Zhang T, Antunes H and Aggarwal S. Defending connected vehicles against malware: Challenges and a solution framework. IEEE Internet Things J 2014; 1 : 0–21. [Google Scholar]
  11. Taeihagh A and Lim HSM. Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transp Rev 2019; 39: 103–28. [CrossRef] [Google Scholar]
  12. Schmittner C, Dobaj J and Macher GA et al. A preliminary view on automotive cyber security management systems. In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020, 1634–39. [Google Scholar]
  13. Wang Y, Wang Y and Qin H et al. A systematic risk assessment framework of automotive cybersecurity. Automot Innovation 2021; 4: 253–61. [CrossRef] [Google Scholar]
  14. Gao C, Wang G, Shi W et al. Autonomous driving security: State of the art and challenges. IEEE Internet Things J 2021; 9: 7572–95. [Google Scholar]
  15. GKhan SK, Shiwakoti N and Stasinopoulos P. A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles. Accid Anal Prev 2022; 165: 106515. [CrossRef] [PubMed] [Google Scholar]
  16. Sun X, Yu FR and Zhang P. A survey on cyber-security of connected and autonomous vehicles (CAVs). IEEE Trans Intell Transp. Syst 2021; 23: 6240–59. [Google Scholar]
  17. Guo L and Ye J. Cyber-physical security of electric vehicles with four motor drives. IEEE Trans Power Electron 2020; 36: 4463–77. [Google Scholar]
  18. Guo L, Yang B and Ye J et al. Attack-resilient lateral stability control for autonomous in-wheel-motor-driven electric vehicles. In: 2021 IEEE Transportation Electrification Conference & Expo (ITEC). IEEE, 2021, 200–5. [CrossRef] [Google Scholar]
  19. Guo L, Yang B and Ye J et al. Attack-resilient lateral stability control for four-wheel-driven EVs considering changed driver behavior under cyber threats. IEEE Trans Transp Electrif 2022; 8: 1362–75. [CrossRef] [Google Scholar]
  20. Wise D. Vehicle cybersecurity: DOT and industry have efforts under way but DOT needs to define its role in responding to a real-world attack, Gao Reports, 2016. https: //www.gao.gov/products/gao-16-350. [Google Scholar]
  21. Nanda A, Puthal D and Rodrigues JJPC et al. Internet of autonomous vehicles communications security: overview, issues, and directions. IEEE Wireless Commun 2019; 26: 60–5. [CrossRef] [Google Scholar]
  22. Chattopadhyay A, Lam KY and Tavva Y. Autonomous vehicle: Security by design. IEEE Trans Intell Transp Syst 2020; 22: 7015–29. [Google Scholar]
  23. Alipour-Fanid A, Dabaghchian M and Zeng K. Impact of jamming attacks on vehicular cooperative adaptive cruise control systems. IEEE Trans Veh Technol 2020; 69: 12679–93. [CrossRef] [Google Scholar]
  24. Guo L, Yang B and Ye J et al., Systematic assessment of cyber-physical security of energy management system for connected and automated electric vehicles. IEEE Trans Ind Inf 2020; 37: 3335–47. [Google Scholar]
  25. Mousavinejad E, Yang F and Han QL et al. Distributed cyber attacks detection and recovery mechanism for vehicle platooning. IEEE Trans Intell Transp Syst 2019; 21: 3821–34. [Google Scholar]
  26. Limbasiya T, Teng KZ and Chattopadhyay S et al. A systematic survey of attack detection and prevention in Connected and Autonomous Vehicles. Veh Commun 2022; 100515. [Google Scholar]
  27. Dasgupta S, Rahman M and Islam M et al. A sensor fusion-based GNSS spoofing attack detection framework for autonomous vehicles. IEEE Trans Intell Transp Syst 2022; 23: 23559–72. [CrossRef] [Google Scholar]
  28. Zhang K, Su R and Zhang H et al. Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework. IEEE Trans Neural Networks Learn Syst 2021; 32: 5502–11. [CrossRef] [PubMed] [Google Scholar]
  29. Wang Y, Bian N and Zhang L et al. Resilient pathfollowing control of autonomous vehicles subject to intermittent denial-of-service attacks. IET Intell Transp Syst 2021; 15: 1508–21. [CrossRef] [Google Scholar]
  30. Boddupalli S, Rao AS and Ray S. Resilient cooperative adaptive cruise control for autonomous vehicles using machine learning. IEEE Trans Intell Transp Syst 2022; 23: 15655–72. [CrossRef] [Google Scholar]
  31. Dongfeng Motor. 2 Gold, 1 Silver, and 2 Challenge Awards. Fully demonstrated Dongfeng Fengshen L3-ADAS. http://www.whkfq.gov.cn/xwzx/yw/kfqyw/qnxw/202206/t202206281995169.html. [Google Scholar]
  32. Hoehn A and Zhang P. Detection of covert attacks and zero dynamics attacks in cyber-physical systems. In: 2016 American Control Conference (ACC), 2016, 302–7. [Google Scholar]
  33. Alieyan K, Kadhum M and Anbar M et al. An overview of DDoS attacks based on DNS. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), 2016, 276–80. [CrossRef] [Google Scholar]
  34. Peters SC and Iagnemma K. Stability measurement of high-speed vehicles. Veh Syst Dyn 2009; 47(6): 701–20. [CrossRef] [Google Scholar]
  35. Jiang Y, Wu S and Yang H et al. Secure data transmission and trustworthiness judgment approaches against cyberphysical attacks in an integrated data-driven framework. IEEE Trans Syst Man Cybern: Syst 2022; 52(12): 7799–809. [CrossRef] [Google Scholar]
  36. Ward D, Ibarra A ann Ruddle A. Threat analysis and risk assessment in automotive cyber security. SAE Int J Passeng Cars – Electron Electr Syst 2013; 6(2): 507–13. [CrossRef] [Google Scholar]
  37. Othmane LB, Fernando R and Ranchal R et al. Likelihood of threats to connected vehicles. Int J Next-Gener Comput 2014; 5(3): 290–303. [Google Scholar]
  38. Petit J and Shladover E. Potential cyberattacks on automated vehicles. IEEE Trans Intell Transp Syst 2015; 16(2): 546–56. [Google Scholar]
  39. Bayer S, Enderle T and Oka DK et al. Security crash test – practical security evaluations of automotive onboard IT components. Automot Saf Secur 2015; 240: 125–39. [Google Scholar]
  40. Georg M, Eric A and Eugen B et al. Threat and risk assessment methodologies in the automotive domain. Procedia Comput Sci 2016; 83: 1288|94. [Google Scholar]
  41. Islam MM, Lautenbach A and Sandberg C et al. A risk assessment framework for automotive embedded systems. In: Proceedings of the 2nd ACM International Workshop on Cyber-Physical System Security, 2016, 3–14. [CrossRef] [Google Scholar]
  42. Alcaraz C, Lopez J and Wolthusen S. OCPP protocol: security threats and challenges. IEEE Trans Smart Grid 2017; 8(5): 2452–59. [CrossRef] [Google Scholar]
  43. Cheah M, Shaikh SA and Bryans J et al. Building an automotive security assurance case using systematic security evaluations. Comput Secur 2018; 77: 360–79. [CrossRef] [Google Scholar]
  44. Morris D, Madzudzo G and Garcia-Perez A. Cybersecurity and the auto industry: the growing challenges presented by connected cars. Int J Automot Technol Manage 2018; 18(2): 105–18. [CrossRef] [Google Scholar]
  45. Bolovinou A, Atmaca UI and Sheik AT et al. TARA+: controllability-aware threat analysis and risk assessment for L3 automated driving systems. In: IEEE Intelligent Vehicles Symposium (IV), 2019, 8–13. [Google Scholar]
  46. Khan SK, Shiwakoti N and Stasinopoulos P. A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles. Accid Anal Prev 2022; 165: 106515. [CrossRef] [PubMed] [Google Scholar]
  47. Moukahal L, Zulkernine M and Soukup M. AVSDA: Autonomous vehicle security decay assessment. In: Risks and Security of Internet and Systems: 16th International Conference, 2022, 20–37. [Google Scholar]
  48. Ying X, Sagong SU and Clark A et al. Shape of the cloak: formal analysis of clock skew-based intrusion detection system in controller area networks. IEEE Trans Inf Forensics Secur 2019; 14(9): 2300–14. [CrossRef] [Google Scholar]
  49. VanWyk F, Wang Y and Khojandi A et al. Real-time sensor anomaly detection and identification in automated vehicles. IEEE Trans Intell Transp Syst 2019; 21(3): 1264–76. [Google Scholar]
  50. Olufowobi H, Young C and Zambreno J et al. SAIDuCANT: specification-based automotive intrusion detection using controller area network (CAN) timing. IEEE Trans Veh Technol 2020; 69(2): 1484–94. [CrossRef] [Google Scholar]
  51. Xie G, Yang LT and Liu Y et al. Security enhancement for real-time independent in-vehicle CAN-FD messages in vehicular networks. IEEE Trans Veh Technol 2021; 70(6): 5244–53. [CrossRef] [Google Scholar]
  52. Liu J and Park J. Seeing is not always believing: detecting perception error attacks against autonomous vehicles. IEEE Trans Dependable Secure Comput 2021; 18(5): 2209–23. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.