The artificial intelligence on cloud platform for cybersecurity using LightGBM model through NIST cybersecurity framework.

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]