A Systematic Review of Anomaly Detection Using Machine and Deep Learning Techniques

Abstract

Anomaly detection identifies objects or events that do not behave as expected or correlate with other data points. Anomaly detection has been used to identify and investigate abnormal data components. Detecting anomalous activities is challenging due to insufficient data size of anomalous reality, ground training data, factors related to differences in environmental conditions, working position of capturing cameras, and illumination situations. Anomaly detection has enormous applications that include (but not limited to) industrial damage prevention, sensor network, health-care services, traffic surveillance, and violence prediction. Machine learning techniques, particularly deep learning has enabled tremendous advancements in the area of anomaly detection. In this paper, we sort out an all-inclusive review of the up-to-date research on anomaly detection techniques. We seek to serve as an extensive and comprehensive review of machine and deep learning anomaly detection techniques throughout the foregoing three years 2019-2021. Particularly, we discuss both machine learning and deep learning anomaly detection applications, performance measurements, and anomaly detection classification. We also point out various datasets that have been applied in anomaly detection along with some fairly new real-world datasets. Finally, we investigate current challenges and future research prospects in this area.