Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles

Abstract

The deployment of connected and automated vehicles (CAVs) may enhance operations and safety with little human feedback. Automation requires the use of communication and smart devices, thus introducing potential access points for adversaries. This paper develops a prototype real-time monitoring system for a vehicle to infrastructure (V2I) based CAV system that generates cyberattack data for CAV operations under realistic traffic conditions. The monitoring system detects any deviations from the normal operation of CAVs using a long-short term memory (LSTM) neural network proposed by the authors and reverts the system back to a safe state of operation using a set of countermeasures. The proposed algorithm was also compared to convolutional neural network (CNN) and other classical algorithms. The monitoring system detected three different emulated cyberattacks with high accuracy. The LSTM showed the highest accuracy of 98% and outperformed the other algorithms. Further, the performance of the monitoring systems was assessed in terms of the impact on traffic stream stability and safety. The results reveal that a fake basic safety message (BSM) attack on even a single CAV causes the traffic stream to become significantly unstable and increase safety risk without the monitoring system. The monitoring system, however, reverts the system to a safe state of operation and reduces the negative impacts of cyberattacks. The monitoring system improves flow stability by an average of 38% as quantified through acceleration variation and volatility. This is comparable to the base case without attacks. The findings have implications for the design of future resilient systems.