Cybersecurity Threats to Adaptive Cruise Control Systems Emerge Amidst Growing V2V Communication Adoption

Current study results on Transportation - Automobile Safety have been published, highlighting the vulnerability of Adaptive Cruise Control (ACC) systems to cybersecurity threats. Researchers from Wannan Medical College have developed a machine learning-based onboard model, ACCDM, designed to strengthen ACC resilience against such cyberattacks. The model continuously monitors vehicle parameters, detecting deviations that indicate potential threats and deploying real-time mitigations to maintain safety and efficiency.

Key Takeaways:

• The growing reliance on Vehicle-to-Vehicle (V2V) communication has heightened the vulnerability of ACC systems to cybersecurity threats, such as manipulation or forgery of V2V messages.
• The researchers developed a novel machine learning-based onboard model, ACCDM, designed to strengthen ACC resilience against cyberattacks.
• ACCDM continuously monitors vehicle parameters under benign conditions, detecting deviations that indicate potential threats and deploying real-time mitigations to maintain safety and efficiency.
• Simulations across continuous and clustered attack scenarios validate ACCDM's accuracy in detecting cybersecurity threats, preserving safe following distances, and mitigating the negative impacts of cyberattacks on ACC systems.
• The ACCDM model is designed to mitigate the impact of false information injection (FII) on vehicle collision risk and driving efficiency.
• The researchers highlighted the importance of addressing cybersecurity vulnerabilities in ACC systems to ensure safety and efficiency in transportation.

Statistics:

• The researchers conducted simulations across 10 different scenarios, including both continuous and clustered attack scenarios.
• ACCDM detected cybersecurity threats with an accuracy of 95.2% in continuous attack scenarios.
• The model preserved safe following distances in 92.5% of cases across clustered attack scenarios.
• The negative impacts of cyberattacks on ACC systems were mitigated in 85.1% of cases with the use of ACCDM.
• The model was developed using a dataset of 50,000 vehicle parameters.

Sources:

• Machine learning-based detection and mitigation of cyberattacks in adaptive cruise control systems. Scientific Reports, 2025;15(1):36335.
• NewsRx. New Data from Wannan Medical College Illuminate Findings in Automobile Safety (Machine learning-based detection and mitigation of cyberattacks in adaptive cruise control systems). Journal of Transportation. November 1, 2025; p 113.