Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey

• URL: http://arxiv.org/abs/2503.13195v1
• Date: Mon, 17 Mar 2025 14:04:48 GMT
• Title: Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey
• Authors: Haoqi Huang, Ping Wang, Jianhua Pei, Jiacheng Wang, Shahen Alexanian, Dusit Niyato,
• Abstract summary: As datasets become more complex, traditional anomaly detection methods struggle to capture intricate patterns.<n>Deep learning has made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data.<n>This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning.
• Score: 43.75849983150303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security breaches, or fraud. As datasets become more complex and high-dimensional, traditional detection methods struggle to effectively capture intricate patterns. Advances in deep learning have made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data. This survey provides a comprehensive review of over 180 recent studies, focusing on deep learning-based AD techniques. We categorize and analyze these methods into reconstruction-based and prediction-based approaches, highlighting their effectiveness in modeling complex data distributions. Additionally, we explore the integration of traditional and deep learning methods, highlighting how hybrid approaches combine the interpretability of traditional techniques with the flexibility of deep learning to enhance detection accuracy and model transparency. Finally, we identify open issues and propose future research directions to advance the field of AD. This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning.

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