Vol. 2 No. 1 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Deep Learning-based Anomaly Detection for Cybersecurity in Autonomous Vehicles

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  • Dr. Sunita Singh
Dr. Sunita Singh
Associate Professor of Computer Science, Indian Institute of Technology Delhi (IIT Delhi)
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Published 30-06-2022

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Sunita Singh, “Deep Learning-based Anomaly Detection for Cybersecurity in Autonomous Vehicles”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, pp. 108–127, Jun. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/66

Abstract

] Connected and automated vehicles (CAVs) are expected to be more secure compared to traditional vehicles because of their infrastructural support and self-awareness due to on-vehicle technologies. At the same time, new security vulnerabilities in sensing, hardware, communication, and environment in CAVs cannot be ignored. New threats like ransom and denial of privacy might occur based on those vulnerabilities. Moving forward, achieving the goal of a complete driverless and connected ecosystem will need to address those and similar new security issues to provide trust in related technologies.[2] Several privacy and security issues are emerging with connected and automated vehicles growing popularity. It is important to enhance vehicle security measures. To ensure the safety of connected and automated vehicles, it is important to detect potential security threats and ensure data security as well as vehicle safety. The paper mainly focuses on protecting vehicle sensor data from potential adversaries. When adversaries lie to sensor data, we say the sensor data is attacked with falsified data. To detect such cyber attacks, we propose two approaches: one is detected with natural state formulation and the other is detected with an augmented state formulation.

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