Anomaly detection is an important problem that has been fundamentally researched in various research and application domains. This survey aims three-fold; firstly, we present a structured and comprehensive overview of anomaly detection methods. Secondly, we review the applications and their effectiveness across various domains. Furthermore, we provide a practical implementation and use of these methods. We grouped current methods into different categories based on the fundamental approach. For each category, we discuss key assumptions used by the methods to differentiate between normal and abnormal behavior. The automatic detection and alerting of abnormal data and behaviors, implemented using computationally efficient software, are critical in this context. These considerations motivate applying the anomaly detection methods to real-world data discussed in this survey. This outline provides an easier and more brief understanding of the methods belonging to each category. We hope this survey will provide a great understanding of the various directions in which research has been done on this topic and how methods developed in one area can be applied in other domains.

@article{Gujral2023, author = "Ekta Gujral", title = "{Survey: Anomaly Detection Methods}", year = "2023", month = "3", url = "https://figshare.com/articles/preprint/Survey_Anomaly_Detection_Methods/22290466", doi = "10.6084/m9.figshare.22290466.v1" }