Analysis of an Intelligent and Cybersecurity Optimization Model for Financial Applications

Recently, machine learning (ML) and artificial intelligence (AI) techniques have been mainly applied to manage and secure data in the various sectors of financial enterprises. Several countries have developed their cyber regimes by applying these soft computing techniques to identify the attacks and threats in running the businesses. Cyber security (CS) applications are developed to detect network intrusion, and still, there are some research gaps in adopting these techniques in some countries. This research explores applying the qualitative and quantitative methods and the issues in implementing the new CS model to manage and promote financial and business growth. This research focuses on developing a new ML model for the business and financial data protection distribution around CS. This research's primary and secondary data collection analyzes that the proposed model behaves well compared to the existing potential CS packages with high-level adoption in business enterprises. Based on the policies and collaborations proposed by the governments, the research model minimizes the risks, threats, and attacks in the small and medium -scale business and financial sectors. The model rightly balances the impact and risk versus cost and productivity impact in securing from cyber threats and attacks. The system vulnerabilities are reduced to less than 3 %. The mean detection time is less than 5 milliseconds, and the mean response time is less than 3 milliseconds. The higher access users range from 1250 to 2500. The expected absolute error lies between 1 % and 5%, and the model's accuracy lies between 92% and 96%.