Overview of Network Security and Risk Assessment for Intelligent Connected Vehicles

This paper investigates the cybersecurity risks in vehicle networking systems, comparing signature-based, anomaly-based, and deep learning-based intrusion detection technologies. It evaluates their applicability, accuracy, real-time performance, and resource consumption, and explores existing limitations. A tailored cybersecurity risk assessment system for intelligent connected vehicles is also summarized, with future threats anticipated. This study provides a systematic review, technology comparison, and assessment framework for the field.