Advancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses

As industrial automation increasingly relies on artificial intelligence (AI) to drive robotic and drone technologies, the need to secure these systems against sophisticated cyber threats has become paramount. By exploring the cybersecurity challenges and solutions for AI-powered industrial systems, AI has become key for advancing real-time threat detection and adversarial machine learning attacks. The implementations of secure AI-driven robotics and drones reach various industrial sectors such as manufacturing, energy, logistics, and agriculture. AI is transforming industrial automation and, at the same time, exposing these systems to new vulnerabilities. Advancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses bridges the gap between the technical aspects of AI, industrial automation, and the evolving landscape of cybersecurity. This book provides readers with insight into the most recent advancements in AI-powered security tools, explore ethical and regulatory considerations, and learn practical strategies to protect complex systems from cyberattacks. Covering topics such as smart factories, wearable devices, and drone systems, this book is an excellent resource for cybersecurity professionals, computer engineers, industrial engineers, policymakers, policy regulators, professionals, researchers, scholars, academicians, and more.

Rajeev Kumar is a distinguished academic administrator with over 15 years of experience in fostering excellence in professional education. He is currently serving as a PostDoc Fellow Infrastructure University, Kuala Lumpur, Malaysia and Professor in the Computer Science and Engineering Department at Moradabad Institute of Technology, Moradabad, Uttar Pradesh, India. Prof. Kumar holds a Ph.D. in Computer Science, a D.Sc. (Post-Doctoral Degree) in Computer Science, and a Postdoctoral Fellowship from Malaysia. His academic credentials are further bolstered by certifications in Data Science and Machine Learning using Python and R Programming from IIM Raipur, as well as additional certifications from IBM and Google. A senior member of IEEE, he actively participates in the IEEE Young Professional Committee and holds memberships in the Computer Society of India (CSI) and SMIEEE. His academic areas of interest and specialization include Artificial Intelligence, Cloud Computing, e-Governance, and Networking. Prof. Kumar has made significant contributions to curriculum development and enrichment, often delivering expert talks and participating in various leadership training programs.

Ashutosh Kumar Bhatt is working as Associate Professor in School of Computer Science and Information Technology, Uttarakhand Open University, Haldwani. He is also Director In-charge, School of Vocational Education and ICT, and Deputy Controller of Examination. His work area of research was Artificial Neural Network. He has more than 17 years of teaching and research experience in various organizations of repute for PG & UG courses of Computer Science & IT. More than 12 SCIE/ESCI and more than 55 Scopus indexed research publications are credited to him in reputed National/International Journals. He is life member of CSI(Computer Society of India) and previously member of IEEE. He had served as State Student Coordinator of Region I Uttarakhand of CSI (for three year) and also served as Secretary of Uttarakhand ACM Professional Chapter. He is awarded by the significant contribution award by CSI for the year 2012-13 and Distinguished Professor award by CSI Mumbai Chapter. He has published and edited number of books e.g. C# Programming using Dot Net Framework, Technology Enabled Learning, Introduction to Computers with basics of Programming, Artificial Intelligence for Societal Development and Global Well-Being etc.

Muneer Khan is an accomplished professional innovator in the field of Electrical Engineering, with a strong background in both academia and industry. He holds a Master of Science in Electrical Engineering from Columbia University, specializing in intelligent and connected devices, sensors, Embedded AI, semiconductor physics, and integrated photonics. Khan has extensive experience in research and applied science, having worked as a Research Assistant at Columbia University’s Laboratory of Unconventional Electronics. He has completed internships in France and India. His work spans advanced technologies, including PCB design, machine learning algorithms, hardware inspection, and robotic sensors. In addition to his academic and research roles, Khan is a successful entrepreneur, having founded Cadre Technologies Services. His venture focuses on AI, machine learning, and assistive technologies. He has been recognized for his contributions to science and technology with numerous awards, including the Distinguished Young Scientist Award from the Government of India, the Engineering for Humanity Award in New York for his outstanding innovation for blind individuals, and multiple accolades from academic institutions. He won the First Position at the Wearable Innovation Forum 2025 at Cambridge University, UK, for his groundbreaking work in wearable assistive technology. Khan’s work has led to several patents, publications, and major funding, including the Engineering for Humanity Award 2025 for his startup. His technical skills span programming, circuit design, and advanced testing, and he continues to lead innovative projects and product developments worldwide.

Shantanu Awasthi has an extensive academic background, holding a Ph.D. in Mathematics from North Dakota State University, an M.S. in Mathematics from Virginia State University, and a B.Tech in Electronics and Communication Engineering from Maharaja Agrasen Institute of Technology. His professional experience spans several academic and industry roles, including positions as an Assistant Professor of Data Analytics at Missouri Southern State University and Data Science roles at various institutions, such as Sense 360 and the University of North Dakota. His research interests focus on stochastic processes, machine learning, and deep learning, with publications in journals like the Journal of Safety Research and the Journal of Stochastic Analysis. In addition to his research, Shantanu has presented his work at notable conferences, including the North American Meetings of the Regional Science Association International and the SIAM Northern States Section Student Chapter Conference. His teaching experience covers data science, statistics, and mathematics courses at multiple universities. He has also earned several honors, including a Graduate Student Travel Grant and a Department Research Award. Furthermore, he holds certifications in actuarial sciences, SAS programming, and deep learning.