Cognitive Threat Analysis Frameworks for Autonomous Vehicle Cybersecurity
Cover
PDF

How to Cite

[1]
Dr. Jure Žužemič, “Cognitive Threat Analysis Frameworks for Autonomous Vehicle Cybersecurity”, Journal of AI in Healthcare and Medicine, vol. 2, no. 2, pp. 71–88, Dec. 2022, Accessed: Nov. 13, 2024. [Online]. Available: https://healthsciencepub.com/index.php/jaihm/article/view/50

Abstract

In the future, CAV systems’ architecture will be more complex and will require more attention from the designers in terms of safety and security. How to overcome these challenges and still keep novel features equally available to vehicle users, and how to protect the digital world together with the physical world, are the main government-drawn paths that are good research subjects. In this study [1], authors emphasize the importance of combining cognitive systems’ approaches with vehicle network security, paving the way for security development at the vehicle level. Cybersecurity models at the edge of things in the vicinity of the driver and passengers should be as close to “impossible to break” as possible. In this paper, these models’ cognitive, vehicle-level cyber security proposals are established considering the potential future features that are built step by step and assist the driver, or replace him controlling the vehicle in the future.

PDF

References

[1] M. Scalas and G. Giacinto, "Automotive Cybersecurity: Foundations for Next-Generation Vehicles," 2019. [PDF]

[2] S. M Mostaq Hossain, S. Banik, T. Banik, and A. Md Shibli, "Survey on Security Attacks in Connected and Autonomous Vehicular Systems," 2023. [PDF]

[3] V. Kumar Kukkala, S. Vignesh Thiruloga, and S. Pasricha, "Roadmap for Cybersecurity in Autonomous Vehicles," 2022. [PDF]

[4] C. Oham, R. Jurdak, and S. Jha, "Risk Analysis Study of Fully Autonomous Vehicle," 2019. [PDF]

Tatineni, Sumanth. "Cost Optimization Strategies for Navigating the Economics of AWS Cloud Services." International Journal of Advanced Research in Engineering and Technology (IJARET) 10.6 (2019): 827-842.

Vemoori, Vamsi. "Comparative Assessment of Technological Advancements in Autonomous Vehicles, Electric Vehicles, and Hybrid Vehicles vis-à-vis Manual Vehicles: A Multi-Criteria Analysis Considering Environmental Sustainability, Economic Feasibility, and Regulatory Frameworks." Journal of Artificial Intelligence Research 1.1 (2021): 66-98.

Mahammad Shaik, et al. “Envisioning Secure and Scalable Network Access Control: A Framework for Mitigating Device Heterogeneity and Network Complexity in Large-Scale Internet-of-Things (IoT) Deployments”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, June 2017, pp. 1-24, https://dlabi.org/index.php/journal/article/view/1.

Tatineni, Sumanth. "Deep Learning for Natural Language Processing in Low-Resource Languages." International Journal of Advanced Research in Engineering and Technology (IJARET) 11.5 (2020): 1301-1311.

[9] M. Basnet and M. Hasan Ali, "A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence," 2021. [PDF]

[10] C. Abdulrazak, "Cybersecurity Threat Analysis And Attack Simulations For Unmanned Aerial Vehicle Networks," 2024. [PDF]

[11] V. Linkov, P. Zámečník, D. Havlíčková, and C. W. Pai, "Human Factors in the Cybersecurity of Autonomous Vehicles: Trends in Current Research," 2019. ncbi.nlm.nih.gov

[12] D. Haileselassie Hagos and D. B. Rawat, "Recent Advances in Artificial Intelligence and Tactical Autonomy: Current Status, Challenges, and Perspectives," 2022. ncbi.nlm.nih.gov

[13] A. Dinesh Kumar, K. Naga Renu Chebrolu, V. R, and S. KP, "A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities," 2018. [PDF]

[14] M. Ebrahimi, C. Striessnig, J. Castella Triginer, and C. Schmittner, "Identification and Verification of Attack-Tree Threat Models in Connected Vehicles," 2022. [PDF]

[15] R. Spencer Hallyburton, Q. Zhang, Z. Morley Mao, and M. Pajic, "Partial-Information, Longitudinal Cyber Attacks on LiDAR in Autonomous Vehicles," 2023. [PDF]

[16] F. Berman, E. Cabrera, A. Jebari, and W. Marrakchi, "The impact universe—a framework for prioritizing the public interest in the Internet of Things," 2022. ncbi.nlm.nih.gov

[17] L. Liu, S. Lu, R. Zhong, B. Wu et al., "Computing Systems for Autonomous Driving: State-of-the-Art and Challenges," 2020. [PDF]

[18] A. Biswas and H. C. Wang, "Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain," 2023. ncbi.nlm.nih.gov

[19] S. N. Saadatmand, "Finding the ground states of symmetric infinite-dimensional Hamiltonians: explicit constrained optimizations of tensor networks," 2019. [PDF]

[20] Y. Shao, S. Weerdenburg, J. Seifert, H. Paul Urbach et al., "Wavelength-multiplexed Multi-mode EUV Reflection Ptychography based on Automatic-Differentiation," 2023. [PDF]

Downloads

Download data is not yet available.