Authors :
Manvendra Singh; Amogh Ansh; Ananya Jain; Richa Dubey; Dr. Karthikeyan B
Volume/Issue :
Volume 10 - 2025, Issue 5 - May
Google Scholar :
https://tinyurl.com/2tdmm5rk
DOI :
https://doi.org/10.38124/ijisrt/25may351
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
In an era where the interconnection between the digital and physical realms becomes increasingly complex, the
security of cyber-physical systems (CPS) emerges as a crucial challenge. This paper undertakes a comprehensive analysis
of journal articles on CPS security, examining the diverse methodologies proposed to fortify these systems against
emerging threats. Emphasizing the role of advanced technologies such as artificial intelligence (AI), machine learning
(ML), and blockchain, our study showcases their pivotal contributions to enhancing CPS resilience. Central to our analysis
is the innovative integration of AI and ML with Digital Twins, a strategy that stands out for its ability to concurrently
bolster security and operational efficacy. This study gives further detail a novel application of AI-enhanced Digital Twins,
outlining a methodology for its practical implementation. Through this investigation, the aim is to enrich the scholarly
dialogue on CPS security, advocating for the strategic use of technological advancements to create smarter, safer, and more
efficientsystems.
Keywords :
Cyber-Physical Systems (CPS), Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology, Digital Twins, CPS Security, AI-enhanced Security, Operational Efficiency, Technological Integration.
References :
- A. K. Tyagi, S. U. Aswathy, G. Aghila and N. Sreenath, “AARIN: Affordable, accurate, reliable and innovative mechanism to protect a medical cyber-physical system using blockchain technology,” International Journal of Intelligent Networks, pp. 175-183, 2021.
- S. A. Latif, F. B. X. Wen, C. Iwendi, L.-l. F. Wang, S. M. Mohsin, Z. Han and S. S. Band, “AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systems,” Computer Communications, pp. 274-283, 2022.
- A. K. Tyagi and N. Sreenath, “Cyber Physical Systems: Analyses, challenges and possible solutions,” Internet of Things and Cyber-Physical Systems, pp. 22-33, 2021.
- Z. Lv, D. Chen, R. Lou and A. Alazab, “Artificial intelligence for securing industrial-based cyber–physical systems,” Future Generation Computer Systems, pp. 291-298, 2021.
- Z. Lian, Q. Yang, W. Wang, Q. Zeng, M. Alazab, H. Zhao and C. Su, “DEEP-FEL: Decentralized, Efficient and Privacy- Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems,” IEEE Transactions on Network Science and Engineering, 2022.
- K. Rijswijk, L. Klerkx, M. Bacco, F. Bartolini, E. Bulten, L. Debruyne, J. Dessein, Scotti and G. Brunori , “Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation,” pp. 79-90, 2021.
- Z. ji, S.-H. Yang, Y. Cao, Y. Wang, C. Zhou, L. Yue and Y. Zhang, “Harmonizing safety and security risk analysis and prevention in cyber-physical systems,” Process Safety and Environmental Protection, pp. 1279-1291.
- B. Wang, H. Zhou, G. Yang, X. Li and H. Yang, “Human Digital Twin (HDT) Driven Human-Cyber-Physical Systems: Key Technologies and Applications,” 2022.
- X. Zhou, X. Xu, W. Liang, Z. Zeng, S. Shimizu, L. T. Yang and Q. Jin, “Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber- Physical Systems,” pp. 1377-1385, 2022.
- M. Xu, J. Peng, B. Gupta, J. Kang, Z. Xiong, Z. Li and A. A. A. El-Latif, “Multi-Agent Federated Reinforcement Learning for Secure Incentive Mechanism in Intelligent Cyber-Physical Systems,” IEEE Internet of Things Journal, 2021.
- L. K. Ramasamy , F. Khan, M. Shah, B. V. V. S. Prasad, C. Iwendi and C. Biamba, “Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring,” Sensors, 2022.
- M. Jbair, B. Ahmad, C. Maple and R. Harrison, “Threat modelling for industrial cyber physical systems in the era of smart manufacturing,” Computers in Industry, 2022.
- C. Feng and P. Tian, “Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering,” in 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021.
- A. N. Jahromi, H. Karimipour, A. Dehghantanha and K.-K. Raymond Choo, “Toward Detection and Attribution of Cyber- Attacks in IoT-enabled Cyber-physical Systems,” IEEE Internet of Things Journal, 2021.
- M. Groshev, C. Guimara ̃es, J. Mart ́ın-Pe ́rez and A. de la Oliva, “Towards Intelligent Cyber-Physical Systems: Digital Twin meets Artificial Intelligence,” IEEE Communications Magazine, pp. 14-20, 2021.
In an era where the interconnection between the digital and physical realms becomes increasingly complex, the
security of cyber-physical systems (CPS) emerges as a crucial challenge. This paper undertakes a comprehensive analysis
of journal articles on CPS security, examining the diverse methodologies proposed to fortify these systems against
emerging threats. Emphasizing the role of advanced technologies such as artificial intelligence (AI), machine learning
(ML), and blockchain, our study showcases their pivotal contributions to enhancing CPS resilience. Central to our analysis
is the innovative integration of AI and ML with Digital Twins, a strategy that stands out for its ability to concurrently
bolster security and operational efficacy. This study gives further detail a novel application of AI-enhanced Digital Twins,
outlining a methodology for its practical implementation. Through this investigation, the aim is to enrich the scholarly
dialogue on CPS security, advocating for the strategic use of technological advancements to create smarter, safer, and more
efficientsystems.
Keywords :
Cyber-Physical Systems (CPS), Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology, Digital Twins, CPS Security, AI-enhanced Security, Operational Efficiency, Technological Integration.