A Machine Learning Approach for Fault Detection in Vehicular Cyber-Physical Systems

Abstract

A network of vehicular cyber-physical systems (VCPSs) can use wireless communications to interact with each other and the surrounding environment to improve transportation safety, mobility, and sustainability. However, cloud-oriented architectures are vulnerable to cyber attacks, which may endanger passenger and pedestrian safety and privacy, and cause severe property damage. For instance, a hacker can use message falsification attack to affect functionality of a particular application in a platoon of VCPSs. In this paper, a neural network-based fault detection technique is applied to detect and track fault data injection attacks on the cooperative adaptive cruise control layer of a platoon of connected vehicles in real time. A decision support system was developed to reduce the probability and severity of any consequent accident. A case study with its design specifications is demonstrated in detail. The simulation results show that the proposed method can improve system reliability, robustness, and safety.