Evolution of Anomaly Detection

From Data Analytics to Global Risk Governance
Erscheinungsjahr 2027
ISBN: 978-3-032-43448-7
Verlag: Springer

Buch, Englisch, 229 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Risk, Governance and Society

This book offers a comprehensive and interdisciplinary exploration of anomaly detection, tracing its evolution from classical statistical methods to modern machine learning, deep learning, and hybrid AI approaches. Anomaly detection has become one of the most critical capabilities of the digital age. As societies increasingly rely on interconnected infrastructures, artificial intelligence, financial networks, and automated decision systems, small deviations in data can signal major threats, from cyberattacks and financial fraud to infrastructure failures and systemic crises. Yet despite its growing importance, anomaly detection remains fragmented across disciplines and application domains.

Moving beyond purely technical perspectives, it demonstrates how anomaly detection functions as an essential component of risk governance, organizational resilience, and societal preparedness.

Through a combination of theoretical foundations, practical case studies, and real-world applications, readers will learn how anomalies emerge, why detection systems fail, and how organizations can design more robust strategies for identifying and managing uncertainty. The book covers statistical approaches, adaptive thresholding, supervised and unsupervised learning, fraud detection, representation learning, contrastive learning, and governance-oriented frameworks for high-stakes environments.

Bridging the worlds of data science, cybersecurity, risk management, and public policy, this book is an invaluable resource for researchers, graduate students, AI practitioners, risk professionals, and decision-makers seeking to understand and govern complex digital systems. Ultimately, it argues that anomaly detection is no longer merely a technical challenge, but a foundational capability for managing uncertainty and safeguarding modern society.

Zielgruppe

Research

Autoren/Hrsg.

Maitra, Sarit

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie
• Wirtschaftswissenschaften Betriebswirtschaft Management
• Sozialwissenschaften Politikwissenschaft Politische Systeme Zentralregierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Sozialwissenschaften Politikwissenschaft Politische Systeme Verwaltungswissenschaft, Öffentliche Verwaltung
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik EDV | Informatik Angewandte Informatik

Inhaltsverzeichnis

Foundations of Anomaly Detection in Risk, Governance, and Society.- Understanding Anomalies Across Complex Sociotechnical Systems.- Thresholding, Uncertainty, and Governance.- Machine Learning Foundations for Risk Governance and Anomaly Detection.- Supervised Learning for Risk Assessment and Governance Applications.- Deep Representation Learning for Complex Risk Mitigation.- Model Risk & Governance.

Über Autor(innen)

Sarit Maitra is Ph.D. in information technology from Universiti Teknologi PETRONAS, Malaysia. He is currently affiliated with the Alliance School of Business, Alliance University, Bengaluru, India, as Professor of Business Analytics and Artificial Intelligence. He brings nearly three decades of industry experience, with specialization in data, big data, and business analytics. With deep expertise in data strategy and decision science, he applies both linear and nonlinear modeling approaches to develop simulation, optimization, and decision-support systems, consistently translating complex data into measurable business outcomes. Drawing on his extensive industry experience, he transforms data into actionable insights, leads high-performing teams, and aligns analytics initiatives with organizational goals. He has contributed to several scholarly works and publications in leading academic journals. He also plays a key role in multiple consulting engagements, spearheading analytics strategy and data-driven decision-making to support organizational strategy and business success.