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Nowadays, the technology of computing big data has moved to cloud computing. At the same time, AI technology is incorporated to enable data learning, analysis, and accurate predictions. In this research, the capabilities of this technology were used to study the effectiveness of cloud AI operations in terms of security in accordance with the NIST Cyber security framework. That uses datasets to simulate TCP SYN Flood attacks. This type of attack can be extended to another cluster. Provided the system does not have a resource constraint any more preparedness to respond according to the National Institute of Standards and Technology framework. In addition, the lack of adequate security protection leads to the loss of a huge amount of system resources. Therefore, it uses machine learning in the virtual cloud by implementing AI in the system for performance and can help detect the properties that extract TCP headers from the dataset. The research has shown that cloud AI machine learning algorithms are highly effective in identifying and detecting TCP SYN flood attacks, leading to system protection. [ABSTRACT FROM AUTHOR]

Blockchain technology is used in various industries has gained significant traction due to its ability to enhance fairness, transparency, and security. The gambling industry is no exception, as blockchain gambling has become increasingly popular for its ability to provide players with a transparent and secure gambling experience. In this paper, we propose a novel approach to pre-storing the hash code of all cards using SHA-512 encryption, thereby enabling the prediction of future cards. Our approach faces the challenge of limited storage capacity, requiring up to 406,640 PB to store all the hash codes. However, we have performed simulations on reduced data to demonstrate the feasibility of our approach and to provide a proof of concept for collecting hash codes in a database. [ABSTRACT FROM AUTHOR]