Cybersecurity Threats to Autonomous Vehicles: A Deep Learning Approach
摘要
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.