Enhancing Cyber-Physical Systems Security: A Review of Deep Learning and Blockchain Integration

Cyber-Physical Systems (CPS) combine physical and computational elements to produce intelligent systems communicating with their surroundings. By integrating digital and physical processes, CPS provides increased functionality in various industries, including industry, transportation, and healthcare. However, there are security, data integrity, and privacy issues brought on by the extensive integration of CPS across several domains. Therefore, addressing these issues is significant for an effective and reliable CPS model. This study presents a comprehensive review exploring the synergistic relationship between two cutting-edge technologies, Deep Learning (DL) and Blockchain, deployed to resolve these issues in CPS. This study explores the efficiency of leveraging DL and blockchain to enhance the reliability and effectiveness of the CPS models. Also, this study examines the challenges and limitations faced by those DL and blockchain approaches during their implementation across different CPS domains. This comprehensive review provides promising directions for future research in this dynamic and interdisciplinary field for mitigating the challenges in CPS models. It also aids researchers, practitioners, and policymakers by providing a deeper understanding of the possible effects, difficulties, and opportunities related to utilizing DL and Blockchain technologies to improve the security and functionality of CPS.

I. Introduction

In the contemporary digital landscape, automation has emerged as a pivotal tool to streamline various tasks, enhancing efficiency and productivity. In the realm of academic administration, the integration of automation technologies has revolutionized traditional processes, offering innovative solutions to common challenges. One such advancement is the development of a sophisticated bot designed to manage assignments seamlessly. This assignment bot serves as a multifunctional tool aimed at optimizing the assignment submission process within educational institutions. By enabling automation, the bot effectively reads the incoming emails from students, identifies assignment submissions, and updates a centralized Excel database with pertinent information. Through this automated process, manual data entry errors are minimized, ensuring accuracy and reliability in recordkeeping. Moreover, the assignment bot goes beyond mere data management. It plays a proactive role in monitoring assignment submissions by sending timely notifications to students who have yet to submit their work. This feature serves as a gentle reminder, encouraging students to fulfil their academic requirements promptly and aiding in the timely completion of coursework. Furthermore, the bot caters to the needs of educators by generating comprehensive reports of incomplete submissions.