Deep Learning for Anomaly Detection: A Survey

2019/01/10 by Raghavendra Chalapathy, Chalapathy, Raghavendra, Sanjay Chawla +1 · 2 voices · 71 citations
Computer Science · Mathematics · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Network Security and Intrusion Detection #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1901.03407

openalex publication_date 2019/01/10 · arxiv created 2019/01/23 · arxiv updated 2019/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

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Discussions

• Deep Learning for Anomaly Detection: A Survey (2019) [hn, 5 points, 0 comments]
• Deep Learning for Anomaly Detection: A Survey arxiv.org/abs/1901.03407 (baseline overview) [bsky, 0 points, 0 comments]

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