May 1, 2024
Updated May 11, 2025
28 minute read
Anomaly detection, at its core, is the process of identifying data points, events, or observations that deviate significantly from the expected or normal behavior of a dataset. Think of it as finding the "odd one out" in a large collection of items. This could be an unusually high transaction on a credit card, a sudden spike in network traffic, or a subtle change in a manufacturing process. While the concept might seem straightforward, its applications are vast and increasingly critical in our data-driven world. The field has a rich history, initially rooted in statistical analysis where experts would manually scrutinize charts and data for abnormalities. Today, it heavily leverages the power of artificial intelligence (AI) and machine learning (ML) to automate this process, enabling the analysis of massive and complex datasets.
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Reading list
We've selected 18 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Anomaly Detection.
This is considered a classical textbook in the field of outlier analysis, which is synonymous with anomaly detection. It provides a comprehensive overview of various techniques and must-read for those serious about the topic. It is highly valuable as a primary reference for both students and professionals.
Offers a readable introduction to the principles of anomaly detection, making it suitable for newcomers to the field. It describes a large number of algorithms, highlighting their strengths and weaknesses. It covers techniques for single and multiple time series data as well as multi-dimensional data. This book is helpful for advanced-level computer science students and practitioners.
Focusing on a key application area, this book explores the use of machine learning algorithms specifically for fraud detection in the financial sector. It provides insights into various algorithms and methodologies used to combat financial crimes. is valuable for practitioners and students interested in this domain.
Teaches how to build and deploy fraud detection systems using machine learning, covering various types of fraud and techniques. It provides practical examples using Python and is relevant for those interested in the application of ML to combat fraud.
Offers a practical approach to anomaly detection using Python, with a focus on hands-on examples and real-world applications. It's suitable for those who want to implement anomaly detection techniques. It provides a good balance between concepts and practical implementation.
Examines network anomaly detection from a machine learning perspective. It covers network anomalies, vulnerabilities, and various machine learning techniques for detection. It's particularly relevant for those interested in cybersecurity applications of anomaly detection. The book includes material for hands-on development.
Drawing on experience in fraud fighting, this book provides best practices, methodologies, and tools for detecting and preventing fraud. It covers typical attacks and recommended detection methods, relevant for data scientists, data analysts, and fraud analysts.
Explores the intersection of deep learning and Explainable AI (XAI) for anomaly detection. It addresses the challenge of interpreting deep learning models in this context and provides state-of-the-art methods. This is relevant for those interested in the latest advancements and the interpretability of anomaly detection systems.
Focuses on anomaly detection techniques for security and fraud detection applications. It covers both theoretical concepts and practical implementations, including case studies and real-world examples.
While not solely focused on anomaly detection, this widely used textbook includes a dedicated chapter (Chapter 12 in the 3rd edition) on outlier detection, covering key concepts and techniques. It provides essential background knowledge in data mining, which is foundational for many anomaly detection methods. is commonly used as a textbook in academic institutions.
Provides a focused introduction to ensemble learning techniques specifically for outlier analysis. Ensemble methods are increasingly important in improving the accuracy and robustness of anomaly detection. This book is valuable for those looking to deepen their understanding of advanced techniques.
Provides practical recipes for applying deep learning to time series data, including anomaly detection. It uses PyTorch and covers various deep neural network architectures. It's useful for those with some deep learning background looking to apply it to time series anomaly detection.
Provides a data mining perspective on outlier detection techniques and applications. It covers various methods and good reference for understanding the data mining foundations of anomaly detection.
Focuses on anomaly detection in manufacturing processes using control charts and machine learning. It promotes an interdisciplinary approach and provides algorithms and case studies relevant to the manufacturing industry. It is valuable for those interested in this specific application domain.
A beginner-friendly guide to applying deep learning for anomaly detection using Keras and PyTorch. It covers various deep learning models for semi-supervised and unsupervised anomaly detection tasks. is suitable for those new to deep learning in the context of anomaly detection.
This monograph reviews techniques for outlier detection and analysis, discussing the effect of outliers on statistical parameters and methods for detecting masking and swamping. It also covers outlier detection in multivariate data using clustering. It provides a good overview of statistical aspects.
While not purely technical, this book provides a crucial business and governance perspective on fraud prevention and detection, a major application of anomaly detection. It offers practical advice and frameworks relevant for professionals in finance and compliance. It highlights the real-world impact and context of anomaly detection.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/h9hv91/anomaly
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