STRENGTHENING FINANCIAL CYBERSECURITY THROUGH MACHINE LEARNING BASED THREAT ANALYSIS

Abstract

In light of the increasing frequency and sophistication of cyberattacks on financial institutions, this initiative seeks to enhance cybersecurity measures by employing threat analysis based on machine learning. The rapid digitization of financial services, internet transactions, and payment systems has rendered financial systems increasingly susceptible to fraud, malware, phishing, and unauthorized access. Machine learning algorithms analyze extensive volumes of transactional and network data to identify anomalies and potential vulnerabilities. This is a sophisticated method for identifying and managing potential risks. Algorithms facilitating the rapid identification of suspicious activity and the monitoring of real-time events encompass random forests, neural networks, support vector machines, and anomaly detection models. Machine learning-based cybersecurity frameworks employ adaptive learning and predictive analytics to enhance threat intelligence, accelerate response times, and fortify the resilience of financial institutions. Cybersecurity systems and cognitive threat analysis collaborate to mitigate risks preemptively, enhance data protection, and ensure the security of online financial transactions.