A data-driven method for falsified vehicle trajectory identification by anomaly detection

The vehicle-to-infrastructure (V2I) communications enable a wide range of new applications, which bring prominent benefits to the transportation system. However, malicious attackers can potentially launch falsified data attacks against V2I applications to jeopardize the traffic operation. To ensure the benefits brought by the V2I applications, it is critical to protect the applications from those cyber-attacks. However, existing literature on the defense solution that protects the V2I applications is very limited. This paper aims to fill this research gap by proposing a datadriven method to identify falsified trajectories generated by compromised connected vehicles (CVs). A trajectory embedding model, inspired by the word embedding model from the natural language processing (NLP) community, is developed. The proposed trajectory embedding model generates vector representations of vehicle trajectories that can be used to compute the similarities between trajectories. The proposed method consists of two steps. In the first step, historical trajectory data are used to train a neural network and obtain the vector representations of trajectories. The second step computes a distance matrix between each pair of trajectories and identifies falsified trajectories using a hierarchical clustering algorithm. Simulation experiments show that the proposed method has a very high detection rate (>97.0%) under different attack goals with varying CV penetration rates from 100% to 25%, while the false alarm rate remains low. It has great potential to be implemented in a wide range of trajectory-based CV applications such as traffic state estimation and traffic signal control, to safeguard the CV system from cyber threats.

Automated summary

This paper proposes a data-driven method to identify falsified trajectories generated by compromised connected vehicles, achieving high detection rates and low false alarm rates through the use of a trajectory embedding model and hierarchical clustering algorithm.