# Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey

**Authors:** Haoqi Huang, Ping Wang, Jianhua Pei, Jiacheng Wang, Shahen Alexanian, Dusit Niyato  
**arXiv:** [2503.13195](https://arxiv.org/abs/2503.13195)  
**Date:** 2025-03-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2503.13195v1/

## Key points

- The rapid expansion of data from diverse sources has increased the importance of anomaly detection (AD) for identifying system failures, security breaches, and fraudulent activities.
- Traditional detection methods struggle with complex and high-dimensional datasets, but deep learning advancements have improved AD techniques.
- A comprehensive survey titled "Deep Learning Advancements in Anomaly Detection" reviews over 180 recent studies on deep learning-based AD techniques.
- The survey categorizes these techniques into two main approaches: reconstruction-based and prediction-based, known for accurately modeling complex data distributions.
- Integration of traditional and deep learning methods creates hybrid approaches that enhance detection accuracy while maintaining model transparency.
- The authors identify open issues in AD and propose future research directions to advance these techniques further.

## Summary

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The rapid expansion of data from diverse sources has heightened the importance of anomaly detection (AD) in identifying unexpected observations that may indicate system failures, security breaches, or fraudulent activities. With datasets becoming increasingly complex and high-dimensional, traditional detection methods have struggled to effectively capture intricate patterns. However, recent advancements in deep learning have significantly enhanced AD techniques by making them more powerful and adaptable to handle such challenging data structures. In their comprehensive survey titled "Deep Learning Advancements in Anomaly Detection," authors Haoqi Huang, Ping Wang, Jianhua Pei, Jiacheng Wang, Shahen Alexanian, and Dusit Niyato review over 180 recent studies focusing on deep learning-based AD techniques. The survey categorizes and analyzes these methods into two main approaches: reconstruction-based and prediction-based. These approaches are highlighted for their effectiveness in modeling complex data distributions accurately. Moreover, the survey delves into the integration of traditional and deep learning methods to create hybrid approaches that combine the interpretability of traditional techniques with the flexibility of deep learning. This integration enhances detection accuracy while maintaining model transparency—a crucial aspect in anomaly detection applications. The authors also identify open issues within the field of AD and propose future research directions to advance the capabilities of these techniques further. By bridging existing gaps in literature, this survey serves as a valuable resource for researchers and practitioners aiming to enhance their anomaly detection methodologies using deep learning technologies. Overall, this detailed exploration showcases how deep learning advancements have revolutionized anomaly detection processes by enabling more robust handling of high-dimensional and unstructured data sets.