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Toward Unified Interpretability and Robustness in Machine Learning–Based Anomaly Detection Across Industrial, Network, Financial, and Cyber-Physical Domains

Johannes K. Albrecht, Lukas M. Reinhardt (Author) ¹

Pages: 1-5 || Published: 2026-01-08

• Abstract

  Anomaly detection has emerged as one of the most intellectually complex and practically consequential subfields of machine learning, driven by the accelerating digitization of industrial processes, networked infrastructures, financial systems, and cyber-physical environments. Across these domains, anomalies often represent rare, evolving, and... More >