Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Explainable AI for Transparent Decision-Making in Autonomous Vehicle Cybersecurity Operations

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  • Dr. Andrés Herrera
Dr. Andrés Herrera
Professor of Industrial Engineering, Universidad de los Andes (UNIANDES), Colombia
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Published 30-12-2022

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How to Cite

[1]
Dr. Andrés Herrera, “Explainable AI for Transparent Decision-Making in Autonomous Vehicle Cybersecurity Operations”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 31–52, Dec. 2022, Accessed: Sep. 19, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/77

Abstract

Introduction: We are proposing to conduct fundamental and basic research in Explainable Artificial Intelligence (XAI) for transparent decision-making for cyber-attack response strategy and tactics in autonomous vehicle (AV) cybersecurity operations, as AVs embody a promising domain where XAI promises amplified situation awareness and transparent and explainable decision-making that can be leveraged for application to both the AV and broader domains. The significance and impact of this project also extends to national priorities and national defense as the agencies of the Research, Technology and Development RDT&E community continue to face challenges to conduct cyber-operations in networked environments, and the success of these cyber-operations directly depends on the effectiveness, speed, and defensibility of the cyber-response processes, especially for sectors like transportation and critical infrastructure that draw significant attention from adversaries. This research will be conducted at West Virginia University (WVU) in conjunction with the institution's capabilities in data, data science, artificial intelligence and machine learning, human factors, and autonomous systems, in the Center for Advanced Communications Avionics-Undermanned Systems CAC-AUS and Transportation and Innovation Institute TII, that include a focus on autonomous transportation systems.

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References

  1. A. Rahim, T. N. Gia, and M. N. S. Swamy, "Explainable AI for Transparent Decision-Making in Autonomous Vehicle Cybersecurity Operations," in IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 6902-6915, July 2021, doi: 10.1109/TVT.2021.3082741.
  2. Y. Kim, J. Kim, and H. Kim, "Interpretable Machine Learning for Automotive Cybersecurity," in 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 2019, pp. 1-10, doi: 10.1109/RTCSA.2019.00014.
  3. F. Liu, Q. He, and K. Zeng, "Explainable Machine Learning for Cyber-Physical Security of Autonomous Vehicles," in 2020 IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 2020, pp. 599-604, doi: 10.1109/ICPS48472.2020.00085.
  4. A. R. Rodrigues, D. B. Rawat, and A. M. Alberti, "Explainable Artificial Intelligence in Cyber-Physical Systems: A Survey," in IEEE Access, vol. 9, pp. 34932-34953, 2021, doi: 10.1109/ACCESS.2021.3069383.
  5. Y. Park, J. Lee, and S. Choi, "Interpretable Machine Learning for Secure and Resilient Automotive Cyber-Physical Systems," in IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 1364-1372, Feb. 2021, doi: 10.1109/TII.2020.3005073.
  6. Tatineni, Sumanth. "Customer Authentication in Mobile Banking-MLOps Practices and AI-Driven Biometric Authentication Systems." Journal of Economics & Management Research. SRC/JESMR-266. DOI: doi. org/10.47363/JESMR/2022 (3) 201 (2022): 2-5.
  7. Vemori, Vamsi. "Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis." Advances in Deep Learning Techniques 1.1 (2021): 23-57.
  8. Leeladhar Gudala, et al. “Leveraging Artificial Intelligence for Enhanced Threat Detection, Response, and Anomaly Identification in Resource-Constrained IoT Networks”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019, pp. 23-54, https://dlabi.org/index.php/journal/article/view/4.
  9. A. M. Tanenbaum, D. J. Wetherall, "Computer Networks," 5th ed., Pearson, 2011, pp. 96-103, ISBN: 0132126958.
  10. J. Smith, "How to Secure Your Connected Car from Hackers," in IEEE Consumer Electronics Magazine, vol. 7, no. 2, pp. 103-108, March 2018, doi: 10.1109/MCE.2018.2797507.
  11. G. Liao et al., "Security and Privacy in Internet of Vehicles," in IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3620-3629, June 2019, doi: 10.1109/TII.2018.2879210.
  12. A. R. Khan, M. U. Ilyas, and S. A. Madani, "Vehicular Ad Hoc Networks: A New Challenge for AI-Based Intelligent Transport Systems," in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 947-956, March 2019, doi: 10.1109/TITS.2018.2824944.
  13. M. A. Al-Rodhaan et al., "A Survey of Machine Learning in Internet of Things," in Journal of King Saud University - Computer and Information Sciences, vol. 31, no. 3, pp. 345-376, July 2019, doi: 10.1016/j.jksuci.2018.02.006.
  14. A. R. Kang, K. S. Yap, and A. U. H. Sheikh, "Machine Learning in Internet of Things (IoT): Recent Advances, Trends and Challenges," in Electronics, vol. 8, no. 3, p. 227, Feb. 2019, doi: 10.3390/electronics8030227.
  15. R. R. Mallepogu, V. K. Rakamarić, and P. R. Kumar, "Machine Learning for Wireless Communication Security in Internet of Things," in IEEE Access, vol. 7, pp. 176047-176069, 2019, doi: 10.1109/ACCESS.2019.2952423.
  16. P. N. Pathirana, A. J. C. Morello, and K. D. B. Madanayake, "Deep Learning for Cyber Physical Systems Security: A Survey," in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1991-2018, April 2019, doi: 10.1109/JIOT.2018.2874887.
  17. L. Gao et al., "Deep Learning in Smart Agriculture: A Review," in Computers and Electronics in Agriculture, vol. 158, pp. 243-263, March 2019, doi: 10.1016/j.compag.2019.01.009.
  18. A. S. Mohan et al., "Deep Learning Based Energy Efficient Data Security for Internet of Things in Smart Grid," in IEEE Access, vol. 7, pp. 153383-153392, 2019, doi: 10.1109/ACCESS.2019.2948962.
  19. S. Mukherjee et al., "Deep Learning for Smart Energy Systems: A Survey," in IEEE Transactions on Industrial Informatics, vol. 16