AI-Driven Cyber Threat Intelligence and Real-Time Attack Prevention  System for Financial Institutions Using Behavioral Anomaly Detection and  Continuous Risk Scoring

Rizwana Sindhavani

AI-Driven Cyber Threat Intelligence and Real-Time Attack Prevention System for Financial Institutions Using Behavioral Anomaly Detection and Continuous Risk Scoring

Authors

Rizwana Sindhavani

Keywords:

Cyber Threat Intelligence, Behavioural Anomaly Detection, Continuous Risk Scoring, Financial Cybersecurity, Real-Time Prevention, Machine Learning

Abstract

Financial institutions face an evolving landscape of cyber threats that include credential theft,account takeover, insider fraud, and sophisticated malware campaigns targeting paymentinfrastructure. Traditional rule-based defences struggle to keep pace with the speed and adaptability of modern attackers, leaving critical gaps in detection and response. This paperproposes an artificial intelligence-driven

References

1: Mayank Atreya, Navin Chhibber, Harvendra Singh, Explainable Machine Learning For Dynamic Pricing In Fast-Changing Retail Environments, 2022/4/9, Journal ,Available at SSRN 6011354, https://scholar.google.com/citations?view_op=view_citation&hl=en&user=fyViF1UAAAAJ&citation_for_view=fyViF1UAAAAJ:LkGwnXOMwfcC

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Published

2023-05-20

How to Cite

Rizwana Sindhavani. (2023). AI-Driven Cyber Threat Intelligence and Real-Time Attack Prevention System for Financial Institutions Using Behavioral Anomaly Detection and Continuous Risk Scoring . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 3055–3066. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5677