Adaptive Threat Detection in DevOps: Leveraging Machine Learning for Real-Time Security Monitoring
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Abstract
In the evolving landscape of software development, DevOps practices have become the cornerstone for delivering rapid, reliable, and scalable applications. However, this increased velocity has introduced new security challenges, necessitating robust and adaptive threat detection mechanisms. This paper explores the integration of machine learning techniques into DevOps pipelines to enhance real-time security monitoring. By leveraging adaptive algorithms, the proposed approach dynamically identifies and mitigates security threats within the continuous integration/continuous deployment (CI/CD) process. The study highlights the effectiveness of machine learning in detecting anomalies, predicting potential threats, and automating responses, thus ensuring a proactive security posture. Through case studies and experimental results, we demonstrate how machine learning-driven threat detection can significantly reduce vulnerabilities and enhance the overall security framework within DevOps environments. This research contributes to the growing body of knowledge on securing DevOps pipelines and provides practical insights for implementing machine learning solutions in real-world scenarios
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References
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