LEVERAGING MACHINE LEARNING FOR ANOMALY DETECTION IN BANKING CLOUD ENVIRONMENTS (original) (raw)
The banking sector is witnessing a rapid shift towards cloud technologies to capitalize on scalability, flexibility, and cost-effectiveness. However, this transition brings forth challenges in monitoring and securing the vast volumes of log data generated within cloud environments. Traditional methods for log analysis struggle to cope with the complexity and dynamic nature of cloud logs, necessitating the adoption of advanced techniques such as machine learning for anomaly detection. This research paper explores the application of machine learning algorithms in detecting anomalies within banking cloud logs. By leveraging supervised, unsupervised, and semisupervised learning approaches, machine learning models can effectively identify abnormal patterns indicative of security threats and potential incidents. The paper reviews existing literature on machine learning-based anomaly detection, discusses challenges and best practices specific to banking cloud environments, and presents case studies illustrating successful implementations. Through this study, banking organizations can gain insights into the potential of machine learning for enhancing security monitoring in cloud environments and mitigating cyber threats effectively.
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