A Proposed Framework for Autonomous Cyber Defense Strategies for Electric Vehicle Network

As electric vehicles (EVs) become increasingly prevalent, securing the complex networked systems that enable their operation is of paramount importance. This paper presents an autonomous cybersecurity framework for EV networks that prevents cyber intrusions through a three-layered defense architecture: monitoring, analysis, and automated response. The framework employs real-time monitoring and machine learning for traffic flow analysis, detecting subtle network variations with-out requiring human intervention. The analysis layer leverages signature-based detection and behavioral analytics to identify potential threats, while the response layer automatically executes defensive actions based on predefined playbooks and dynamic risk assessment. The system demonstrates effective threat mitigation while maintaining operational continuity through automated orchestration of security controls. Our proposed solution provides a secure, scalable platform with comprehensive security features necessary for protecting EV networks against evolving cyber threats in an increasingly connected automotive ecosystem.