Developing an ML Model for Detecting the Cyber Attacks in Electric Vehicles

This study shows that electric cars (EVs) are a viable replacement for conventional combustion-engine vehicles, providing lower carbon emissions and more environmentally friendly modes of transportation. Depite the hype, the challenges like cybersecurity flaws and possible cyberattacks are also emerging in EVs. In addition to jeopardizing user privacy and vehicle safety, a number of issues seriously jeopardize the integrity and security of electric cars (EVs). This research offers a unique machine learning (ML)-based detection and prevention system designed to identify cyber threats in electric vehicles (EVs) in response to these problems. The suggested solution examines Controller Area Network (CAN) traffic data using cutting-edge ML algorithms to find unusual patterns suggestive of cyber threats. The solution improves the cybersecurity resilience of electric vehicles (EVs) by including proactive preventive measures and real-time threat detection capabilities. This ensures vehicle safety and guards against new and existing cyber-attacks. Through thorough evaluation and validation procedures, the efficacy of the suggested system is proven, underscoring its strong performance in identifying and averting cyberattacks in electric vehicles. This research helps to create safer and more secure mobility solutions for electric vehicles by implementing proactive cybersecurity measures and integrating cutting-edge ML technology.