An In-Depth Exploration of Anomaly Detection, Classification, and Localization with Deep Learning: A Comprehensive Overview

The ability to identify trends in the data when one set of data is deviating from another is called Data Mining. The development of anomalies has made it possible to identify and avoid malware, as well as several other unlawful practices. Traditional detection strategies have shown strong results However, as deep learning progresses, important findings have emerged over the past few years. In order to summarize existing and the most cutting-edge fraud and intrusion detection strategies, we address these issues depending on the existence of neural networks, from broad to shallower. This paper provides an analysis of the published techniques for anomaly detection, especially on the contribution of deep learning to detection. Methods were sorted according to the kind of DNN included in this study. These classes helped us to categorize the deep learners by how often they’ve been using, in data representation and for differentiating between various types of anomalies. In addition, deep neural networks in specific anomaly detection tasks presented incontrovertible proof of their effective implementation.