The need for in-vehicle cyber security measures in connected vehicles is critical due to increasing security risks.
Implementing intrusion detection and response systems with adaptive detection mechanisms is essential to detect evolving threats.
Constraints on implementing diverse attack scenarios on test vehicles lead to a scarcity of attack-representing data.
A context-aware attack data generator was developed to efficiently create high-quality attack-representing data for in-vehicle network security testing.