Threat Detection in Cloud Banking Using Machine Learning
Abstract: The domain of cloud banking is rapidly evolving, and with that, the sophistication and frequency of cyber threats have escalated. This poses significant challenges to financial stability and customer trust. Machine learning (ML) has emerged as a pivotal technology to combat these security threats, offering advanced capabilities in detecting and mitigating potential risks. This paper delves into the dynamics of threat detection in cloud banking environments and highlights the crucial role of machine learning in identifying and neutralizing cyber threats. By analyzing various ML models and their application in real-world scenarios, the paper provides insights into the effectiveness and efficiency of machine learning algorithms in safeguarding cloud banking platforms. The discussion extends to the integration of ML technologies within the security infrastructure of cloud banking, underscoring the transformative impact of machine learning in enhancing detection capabilities and response mechanisms against evolving cyber threats. Overall, this paper emphasizes the importance of machine learning in securing cloud banking platforms from cyber threats.
Keywords: Cloud Banking, Machine Learning, Threat Detection, Cyber Security, Financial Technology, Anomaly Detection, Predictive Analytics, Artificial Intelligence, Risk Management, Data Protection
How to Cite?: Ravi Jagadish, "Threat Detection in Cloud Banking Using Machine Learning", Volume 13 Issue 3, March 2024, International Journal of Science and Research (IJSR), Pages: 1181-1184, https://www.ijsr.net/getabstract.php?paperid=SR24318150126, DOI: https://dx.doi.org/10.21275/SR24318150126