Cybersecurity Threat Detection in Financial Institution Using AI BasedRisk Assessment

In recent times, the research looks into the measures taken by financial institutions to secure their systems and reduce the likelihood of attacks. The study results indicate that all cultures are undergoing a digital transformation at the present time. The dawn of the Internet ushered in an era of increased sophistication in many fields. There has been a gradual but steady shift in attitude toward digital and networked computers in the business world over the past few years. Financial organizations are increasingly vulnerable to external cyberattacks due to the ease of usage and positive effects. They are also susceptible to attacks from within their own organisation. In this paper, we develop a machine learning based quantitative risk assessment model that effectively assess and minimises this risk. Quantitative risk calculation is used since it is the best way for calculating network risk. According to the study, a network s vulnerability is proportional to the number of times its threats have been exploited and the amount of damage they have caused. The simulation is used to test the model s efficacy, and the results show that the model detects threats more effectively than the other methods.

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