4.4 Article

Understanding the Cyber-Physical System in International Stadiums for Security in the Network from Cyber-Attacks and Adversaries using AI

Journal

WIRELESS PERSONAL COMMUNICATIONS
Volume 127, Issue 2, Pages 1207-1224

Publisher

SPRINGER
DOI: 10.1007/s11277-021-08573-2

Keywords

Artificial Intelligence; Cyber-physical system; Cybersecurity; Attacks; International stadium

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Sports stadiums have significant impacts on the environment, cities, and society. The use of information and communication technology applications in international sports stadiums is increasing, with advancements in AI techniques showing promise in enhancing cybersecurity. The proposed AI-CPS model predicts anomaly behavior in the network and improves security in sports events.
Sports stadiums have a substantial influence on the environmental, urban, and social context. Information and communication technology applications in the international sports stadium increasingly use modern venues and facilities, containing the command and control system, intelligent application of sports facilities, television systems, ticket access control systems, communication systems for event management, contest information systems, etc. There is a high demand for advanced stadium security systems because of the large number of sporting events organized. Hence, in this study, an Artificial intelligence assisted Cyber-Physical System (AI-CPS) has been proposed for security in the network to predict cyber attacks and adversaries. The data has been collected and analyzed, and the proposed AI-CPS model predicts anomaly behaviour in the network. This study deals with the subject of how surveillance and security practices at sports events are organized. Advances in Artificial Intelligence (AI) techniques show potential in enabling cybersecurity authorities to counter the ever-evolving attack posed by an adversary. Here, this paper explores AI's potential in enhancing cybersecurity resolutions by determining both its strengths and weaknesses. The numerical results show that the suggested AI-CPS model improves an accuracy ratio of 95.6%, a prediction ratio of 97.6%, packet loss of 12.3%, delay ratio of 15.1%, and latency ratio of 11.2% to other existing methods.

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