Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial  Applications

Avinash Reddy Segireddy

Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications

Authors

Avinash Reddy Segireddy

Keywords:

Anomaly detection; financial applications; ML; CI/CD pipelines; integrity; compliance,Machine Learning–Based Anomaly Detection,CI/CD Security Monitoring,Financial Software Integrity,Behavioral Analytics for Pipelines,DevSecOps Automation,Pipeline Risk Scoring,Model-Driven Threat Detection,Real-Time Deployment Monitoring,Secure Continuous Delivery,Fraud and Compliance-Aware Pipelines.

Abstract

The effects of machine learning-driven anomalydetection in CI/CD pipelines specific to financial applications are examined. Risk reduction

References

Jia, F., & Zhang, Y. (2024). Supply chain transparency: Opportunities, challenges and risks. International Journal of Operations & Production Management, 44(9), 1525-1549.

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Published

2024-12-20

How to Cite

Avinash Reddy Segireddy. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 2176–2185. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4228