Cyber-Physical Attacks and Defenses in Autonomous Vehicles - A Deep Learning Approach: Analyzes cyber-physical attacks and defenses in AVs, employing a deep learning-based defense mechanism
Published 30-07-2021
Keywords
- Autonomous Vehicles,
- Cyber-Physical Attacks,
- Deep Learning
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Autonomous Vehicles (AVs) are at the forefront of modern transportation, promising increased safety and efficiency. However, their reliance on interconnected systems and sensors makes them vulnerable to cyber-physical attacks. This paper examines the landscape of cyber-physical attacks on AVs and proposes a deep learning-based defense mechanism. We first discuss the types and impacts of such attacks, highlighting the need for robust defenses. Next, we introduce a deep learning approach for detecting and mitigating cyber-physical attacks in real-time. Our proposed system leverages the power of deep neural networks to analyze sensor data and identify anomalies indicative of attacks. We evaluate the effectiveness of our approach through simulations and discuss its implications for securing future AVs.
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References
- 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.
- Tatineni, Sumanth. "Recommendation Systems for Personalized Learning: A Data-Driven Approach in Education." Journal of Computer Engineering and Technology (JCET) 4.2 (2020).