Deep Learning for Cyber Threat Analysis in Robotic Systems

Robotic systems play a critical role in manufacturing healthcare, and transportation industries. However, their reliance on interconnected networks renders them vulnerable to cyber threats, including malware attacks, unauthorized access, and data breaches.Deep learning has emerged as a promising approach to detect and mitigate such threats.This chapter explores the application of deep learning techniques to cyber threat analysis in robotic systems. Various architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), are analyzed for their efficacy. A comparative study of these models and a real-world case study are also presented,highlighting the challenges and future research directions in the domainThis chapter delves into the integration of deep learning technologies with robotic systems to address the escalating challenges of cybersecurity. By presenting a comparative analysis of state-of-the-art techniques, it aims to guide researchers and practitioners toward building more resilient robotic infrastructures.