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.

This paper presents a method for tomographically reconstructing spatially varying three-dimensional atmospheric temperature profiles and wind velocity fields based on passive acoustic travel time measurements between a small unmanned aerial vehicle (UAV) and ground-based microphones. A series of simulations are presented to provide an indication of the performance of the technique. The parametric fields are modeled as the weighted sum of radial basis functions (RBFs) or Fourier series, which also allow local meteorological measurements made at the UAV and ground receivers to supplement any time delay observations. The technique has potential for practical applications such as boundary layer meteorology and theories of atmospheric turbulence and wave propagation through a turbulent atmosphere.

Safe and secure operations of robotic systems are of paramount importance. Aiming for achieving the trusted operation of a military robotic vehicle under contested environments, we introduce a new cyber-physical system based on the concepts of deep learning convolutional neural networks (CNNs). The proposed algorithm is specifically designed to reduce the cyber vulnerability of the Robot Operating System (ROS), a well-known middleware platform widely used in both civilian and military robots. To demonstrate the efficacy of the proposed algorithm, we conduct penetration testing (real-time man-in-the-middle cyber attack) on the GVR-BOT ground vehicle, a military ground robot, developed by the United States Army Combat Capabilities Development Command (CCDC), Ground Vehicle Systems Center. The cyber attack also exploits the vulnerability of the Robot Operating System (ROS) employed in its onboard computer. We collect experimental data and train our CNN based on two different operating conditions, namely, legitimate and malicious conditions. We normalize and convert the network traffic data in the form of RGB or grayscale images. We introduce two different types of windowing techniques, namely, the independent and overlapping sliding epochs to efficiently feed the network traffic data to our CNN system. Our research indicates the efficacy of the proposed algorithm as our proposed cyber intrusion detection system can achieve reasonably high accuracy of $\geq 99$ % and substantially small false-positive rates $\leq$ 2 % supported with minimum detection time. In addition, we also compare and demonstrate the relative merits of our proposed algorithm with respect to the performance of some well-known techniques, namely, ‘bag-of-features’ and Support Vector Machine (SVM) algorithms.