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 insuficient data size of anomalous reality, ground training data, factors related to diferences 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, trafic 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 allinclusive 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.
Page(s):
83-94
Published:
Journal: Quaid-e-Awam University Research Journal of Engineering, Science and Technology, Volume: 20, Issue: 1, Year: 2022
Keywords:
deep learning
,
machine learning
,
Anomaly detection
,
Anomaly localization
,
Convolutional Neural Network