Research and application of Transformer based anomaly detection model: A literature review

TL;DR

This paper provides a comprehensive review of Transformer-based anomaly detection, discussing its principles, applications, datasets, challenges, and future research directions, serving as a valuable resource for researchers in this field.

Contribution

It is the first extensive review focusing specifically on Transformer models applied to anomaly detection, summarizing over 100 key references and highlighting future research trends.

Findings

Detailed insights into Transformer operating principles in anomaly detection

Analysis of application scenarios and evaluation metrics

Identification of key challenges and future research directions

Abstract

Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks.