  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.
