Modeling and Detection of Cyber-Attacks on Highway Networks Using a 2D-LWR Model and Gaussian Processes

This paper considers a class of cyber-attacks that can occur over a two-dimensional road network. Our focus is on scenarios wherein an attacker may hack into a subset of vehicles driving on the road network and create subtle changes in their driving parameters. These hacked vehicles (referred to as malicious vehicles) are subsequently able to modify the driving behavior of the overall vehicle network. The traffic system comprising the mix of malicious and normal vehicles is modeled using a system of coupled Partial Differential Equations (PDEs) in a two-dimensional LWR model. We develop a methodology that combines Gaussian Processes (GP) with this two-species 2D PDE model, and use this method for detecting the presence of such malicious vehicles in the road network. A Bayesian Optimization scheme is employed to determine the optimal choice of basis and kernel functions that constitute the GP. Simulation results demonstrate that this detection architecture performs successful detection of the malicious vehicles, and also their mode of attack on the traffic.