Alert Fatigue Mitigation in Anomaly Detection Systems: A Comparative Study of Threshold Optimization and Alert Aggregation Strategies

Abstract

Alert fatigue represents a critical challenge in modern monitoring systems, where excessive false positive alerts overwhelm operations teams and diminish system reliability. This research presents a comprehensive comparative analysis of threshold optimization and alert aggregation strategies designed to mitigate alert fatigue in anomaly detection systems. Through systematic evaluation of a wide variety of adaptive alert threshold adjustment algorithms and intelligent alert correlation and aggregation techniques, our proposed framework demonstrates significant improvements in operational efficiency. We propose a framework that integrates dynamic threshold adjustment mechanisms with multi-dimensional alert aggregation strategies, achieving a 67% reduction in false positive rates while maintaining 94% true positive rate, namely alert detection accuracy. Experimental results across diverse monitoring scenarios reveal that hybrid approaches combining temporal-based threshold optimization with semantic alert clustering outperform traditional static threshold methods. The research also comes up with novel evaluation metrics for measuring impact of our proposed framework on alert fatigue mitigation and provides practical guidelines for implementing effective alert management solutions in complex monitoring infrastructures.