A Data-Driven Cyber–Physical System Using Deep-Learning Convolutional Neural Networks: Study on False-Data Injection Attacks in an Unmanned Ground Vehicle Under Fault-Tolerant Conditions

Abstract

Leveraging the benefits of deep-learning convolutional neural networks, we introduce a new data-driven cyber–physical system specifically designed to address the vulnerability of middleware software, namely, Robot Operating System (ROS), widely implemented in robotics in both civilian and military domains. As for our research platform, we employ the GVR-BOT unmanned ground vehicle, which is a replicate of the U.S. Army ground robot. We focus our study on the ability of the ground robot to operate under fault-tolerant conditions, making it challenging from the standpoint of cybersecurity to differentiate between legitimate and malicious operations. The GVR-BOT ground vehicle belongs to a class of differential drive ground robots and employs ROS in its onboard computer to interact with users. To facilitate deep learning, we develop a large database of images based on the network-traffic data of ROS, describing the dynamics of the GVR-BOT ground robot under legitimate and malicious operations. We use our image database to train and validate the performance of our deep-learning CNN system. Given a set of RGB/grayscale images describing the normalized time-series data representing the dynamics of the GVR-BOT ground robot, the objective of our proposed cybersecurity algorithm is to safeguard the legitimate operation of the ground robot under fault-tolerant conditions, such that any attempts to compromise its performance (e.g., malicious attacks) can be prevented within the minimum detection time. Our research indicates a promising result as our system is capable of detecting malicious attacks with high accuracy while recognizing its legitimate operations with reasonably small false-positive rates.