# Deep Learning for Anomaly Detection: A Survey

**Authors:** Raghavendra Chalapathy, Sanjay Chawla  
**arXiv:** [1901.03407](https://arxiv.org/abs/1901.03407)  
**Date:** 2019-01-10  
**Summary page:** https://www.summarizepaper.com/en/paper/1901.03407v2/

## Key points

- Anomaly detection is a critical issue across research areas and application domains
- Authors Raghavendra Chalapathy and Sanjay Chawla conducted a survey on "Deep Learning for Anomaly Detection"
- The survey categorizes state-of-the-art research techniques based on assumptions and approaches
- Fundamental anomaly detection techniques, variants, and key assumptions are outlined within each category
- Advantages, limitations, and computational complexity of each technique are discussed
- Open issues in research and challenges in implementing these techniques are highlighted

## Summary

Anomaly detection is a critical issue that has garnered significant attention across various research areas and application domains. In their survey titled "Deep Learning for Anomaly Detection," authors Raghavendra Chalapathy and Sanjay Chawla provide a comprehensive overview of research methods in deep learning-based anomaly detection. The survey aims to present a structured analysis of these methods and evaluate their effectiveness in detecting anomalies across diverse application domains. The authors categorize state-of-the-art research techniques based on underlying assumptions and approaches, providing a detailed examination of each category. Within each category, they outline the fundamental anomaly detection technique, its variants, and key assumptions used to differentiate between normal and anomalous behavior. The survey also discusses the advantages and limitations of each technique, as well as the computational complexity involved in applying them in real-world scenarios. Furthermore, the authors highlight open issues in research and challenges faced when implementing these techniques, shedding light on areas that require further exploration and development. Through delving into deep learning-based anomaly detection methods and their applications across different domains, this survey serves as a valuable resource for researchers and practitioners seeking to enhance anomaly detection capabilities in various contexts.