Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners.

Fuzz testing is a key verification technique for identifying robustness and cybersecurity weaknesses in automotive electronic control units (ECUs). However, conventional CAN-based fuzz testing suffers from extremely low acceptance rates because randomly generated frames often violate protocol constraints such as counters, check-sums, and state dependencies. This study addresses the test-preparation bottleneck by proposing an AI-assisted approach for automated identification of stable operational system states from Controller Area Network (CAN) traces. These states can serve as valid starting points for mutation-based and model-based fuzzing. CAN traces generated in a Hardware-in-the-Loop (HIL) environment were analyzed using multiple publicly accessible large language model (LLM) systems. The objective was to evaluate whether AI/LLM tools can (i) identify unique system states, (ii) compute dwell-time distributions, and (iii) derive state transition maps directly from raw CAN traces and DBC definitions. Additionally, we checked the possibility of these tools to analyze the quality of CAN communication (message cycle time). At the end of the study, we ran experiment tasks using CAN logs taken from a production car. Results show that AI-assisted analysis can extract operational states and transitions with varying levels of agreement with the deterministic baseline, supporting preparatory analysis during fuzzing test preparation. While performance varies across tools, AI support demonstrates strong potential for accelerating and assisting structured fuzz testing workflows.