Detection and Inference of Randomness-based Behavior for Resilient Multi-vehicle Coordinated Operations

TL;DR

This paper presents a framework for detecting misbehaving vehicles and securing communication in multi-vehicle systems using physics-inspired models and side-channel signatures, demonstrated through simulations and experiments.

Contribution

It introduces a novel decentralized detection method and secure communication scheme based on spring-damper physics and hidden signatures for resilient multi-vehicle operations.

Findings

Effective detection of malicious vehicles in simulations.

Secure communication maintains formation integrity under attack.

Framework applicable to ground vehicle formations.

Abstract

A resilient multi-vehicle system cooperatively performs tasks by exchanging information, detecting, and removing cyber attacks that have the intent of hijacking or diminishing performance of the entire system. In this paper, we propose a framework to: i) detect and isolate misbehaving vehicles in the network, and ii) securely encrypt information among the network to alert and attract nearby vehicles toward points of interest in the environment without explicitly broadcasting safety-critical information. To accomplish these goals, we leverage a decentralized virtual spring-damper mesh physics model for formation control on each vehicle. To discover inconsistent behavior of any vehicle in the network, we consider an approach that monitors for changes in sign behavior of an inter-vehicle residual that does not match with an expectation. Similarly, to disguise important information and trigger vehicles to switch to different behaviors, we leverage side-channel information on the state of the vehicles and characterize a hidden spring-damper signature model detectable by neighbor vehicles. Our framework is demonstrated in simulation and experiments on formations of unmanned ground vehicles (UGVs) in the presence of malicious man-in-the-middle communication attacks.