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Title	Cybersecurity Threats To Autonomous Vehicles: A Deep Learning Approach
ID_Doc	17049
Authors	Sathyavathy V.
Year	2025
Published	Communications in Computer and Information Science, 2429 CCIS
DOI	http://dx.doi.org/10.1007/978-3-031-86305-9_3
Abstract	In the upcoming years, autonomous cars are predicted to completely transform transportation by offering greater accessibility, efficiency, and safety. Autonomous vehicles are a critical component of smart mobility, which is a fundamental component of smart cities. Significant new Cybersecurity vulnerabilities are also introduced by the intricate sensor systems, onboard computers, and vehicle-to-infrastructure connectivity needed for self-driving cars. However, human safety and quality of life may be negatively impacted by vulnerabilities in Autonomous vehicles. Automotive Cybersecurity is getting a modulation point. The attacks of cyber incidents have grown drastically, threatening safety and carrying functioning important implications. It is more crucial to protect automobiles, mobility apps, and Internet of Things gadgets from the cyberattacks of self-driving cars in a proactive manner. The motivation of threat actors is changing to focus on having a significant and large-scale control on connected cars and motion assets. Deep Learning is a key element of smart cities, and it is closely coupled with autonomous attacks and defences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Author Keywords	Artificial Intelligence; Autonomous Vehicles; Cyberattack; Cybersecurity; IoT

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