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Toward Unified Interpretability and Robustness in Machine Learning–Based Anomaly Detection Across Industrial, Network, Financial, and Cyber-Physical Domains
Issue Vol. 3 No. 01 (2026): VOLUME 03 ISSUE 01 --- Section Articles
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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 context-dependent deviations whose significance is not merely statistical but operational, economic, and ethical. This research article develops a comprehensive, theory-driven, and empirically grounded synthesis of machine learning–based anomaly detection by integrating insights from industrial process monitoring, network intrusion detection, financial fraud analysis, healthcare Internet of Things security, and large-scale data systems. Drawing on a broad and deliberately heterogeneous body of literature, the article advances a unified interpretive framework that explains why anomaly detection remains resistant to universal solutions despite decades of algorithmic innovation. Particular emphasis is placed on comparative methodological perspectives, including classical statistical approaches, shallow machine learning methods, kernel-based novelty detection, clustering paradigms, and deep learning architectures. The analysis is anchored by contemporary empirical findings in industrial screw driving data, which illustrate how algorithmic performance is inseparable from domain semantics, feature engineering choices, and evaluation protocols (West and Deuse, 2024). Rather than summarizing prior work, the article expands each conceptual strand through historical development, theoretical debate, and critical comparison, exposing persistent tensions between accuracy, interpretability, adaptability, and computational feasibility. The methodology section articulates a text-based comparative research design that synthesizes cross-domain findings without relying on mathematical formalism or visual artifacts, thereby foregrounding epistemological assumptions and methodological limitations. Results are presented as interpretive patterns grounded in literature-based evidence, highlighting recurring phenomena such as sensitivity to hyperparameter tuning, dataset bias, and the contextual ambiguity of ground truth labels. The discussion extends these findings into a broader theoretical discourse on the future of anomaly detection research, arguing that progress depends less on novel architectures than on integrative evaluation philosophies and domain-aware learning paradigms. The article concludes by outlining a research agenda that prioritizes interpretability, cross-domain generalization, and ethical accountability as central criteria for next-generation anomaly detection systems.
Keywords
References
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