Machine Learning for Anomaly Detection: A Review of Techniques and Applications in Various Domains

Ali Rezaei, Hossein Mirzaei

Abstract


Anomaly detection, the process of identifying rare items, events, or observations that deviate significantly from the majority of the data, has become a critical task in various domains such as cybersecurity, healthcare, finance, and industrial systems. With the exponential growth of data, traditional methods of anomaly detection have become increasingly inadequate, leading to the adoption of machine learning (ML) techniques. This paper provides a comprehensive review of machine learning techniques for anomaly detection, focusing on their applications across various domains. We discuss the strengths and limitations of different ML approaches, including supervised, unsupervised, and semi-supervised learning, and highlight the challenges and future directions in this field. The review is supported by three detailed tables that summarize the key techniques, their applications, and performance metrics. The paper concludes with a discussion on the potential of emerging technologies such as deep learning and reinforcement learning in advancing the field of anomaly detection.

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