Abstract

The financial sector has been taking up cloud technology at a fast pace, altering where risks are positioned and making it more difficult to implement security requirements adequately. One of the next-generation online security threats is the malicious mining code. Consequently, this kind of programming is able to cause currency loss by using computer resources secretly and thus resulting in decreased system operation. This research's major goal is to recognise and eliminate the risk factors with a coupling of One-Class Support Vector Machine and Monarch Butterfly Optimisation (COCSVM- MBO), an Artificial Intelligence (AI) method. The technology is capable of discovering patterns representing malicious mining bots via the application of machine learning techniques (Vij & Prashant, 2024). The min-max is implemented for data pre-processing. COCSVM improves security by detecting strange behaviour and patterns in the cloud environment and uncovering malicious mining codes in the banking industry. Through cutting down machinations in cloud systems, malicious code mining detection and information security enhancement, the MBO improves the financial sector security. The data set named FinPile was gathered. It is proposed that the given approach of financial institutions reinforces their resilience against mining attacks by ensuring data security and system integrity. The proposed approach can be able to assist in delivering enhanced resilience and accuracy compared to the traditional classifiers.