Adaptive Machine Learning Algorithms for Anomaly Detection in Enterprise IT Infrastructure

Abstract

The increasing scale and complexity of enterprise IT infrastructures renders traditional rule-based and static anomaly detection systems that are automated as well as manual, incapable of dealing with evolving threats, system dynamics, and concept drift. This paper proposes an adaptable Machine Learning (ML) architecture which can autonomously detect real-time anomalies within critical IT environments, including network, application, and host systems. By employing ensemble learning, streaming models, and drift-aware system architectures, the system detection performance degradation with regard to accuracy, latency, and false positives, gets improved. The approach uses real-world datasets, multi-scenario anomaly detection, and inter-model comparisons based on essential values of F1 score, AUC, and detection delay metrics. The experiments conclusively demonstrate the skillfulness of adaptive ML models over the conventional ways of performing tasks in response time and accuracy, while ensuring scalability and interpretability. This work presents the benefits of incorporating adaptive intelligence into monitoring systems at enterprises and aims to assist in constructing robust anomaly detection pipelines in the ever-changing landscape of IT infrastructures.