Securing ROS Network Traffic Data Against Node Isolation Attacks Using a Deep Learning CNN: An Empirical Study on an Unmanned Ground Vehicle

To ensure the operational integrity of military ground robots in hostile environments, we present a cutting-edge cybersecurity framework that leverages deep learning via a convolutional neural network (CNN). While primarily designed to enhance the security of military ground vehicles, the framework also improves the cybersecurity of the Robot Operating System (ROS), a widely adopted middleware across civilian and military robotic platforms. The GVR-BOT, developed by the United States Army Combat Capabilities Development Command (CCDC) Ground Vehicle Systems Center, serves as the experimental platform. To evaluate the robustness of our system, we conduct real-time penetration testing to identify vulnerabilities within ROS onboard the ground vehicle. Our approach involves training the model on two datasets of normalized network traffic, represented as RGB or grayscale images, to capture vehicle behavior under adversarial conditions. The theoretical foundation is supported by a formal convergence lemma, which guarantees that the CNN model minimizes classification loss under standard smoothness and learning rate assumptions. Experimental results demonstrate the efficacy of our framework, achieving over 98% detection accuracy and under 1% false positive rate, while maintaining minimal detection latency. We also compare different windowing techniques to assess performance trade-offs.