Today, anomaly detection is an important area studied across various fields to recognize unusual observations. This paper aims to explore the role of deep learning (DL) in developing effective anomaly detection systems (ADSs).
This study considers DL mechanisms and their applications in modeling ADSs, discussing critical elements of such systems. This study defines anomalies, identify their primary characteristics and describe three distinct DL-based anomaly detection domains: medical, image and video and cyber-physical systems. This study proposes new classifications for each domain and examines them in terms of datasets, detection methods and implementations.
This study identifies the drawbacks and limitations of current anomaly detection techniques and presents several DL-based taxonomies and performance metrics for detecting anomalies.
The paper discusses the limitations and challenges in existing DL-based anomaly detection techniques, providing insights into areas that require further research.
The study provides practical guidelines for developing and implementing DL-based ADSs across various domains.
Improved ADSs can enhance security, safety and efficiency in medical, image and video and cyber-physical systems, benefiting society as a whole.
This paper offers new classifications for DL-based anomaly detection in medical, image and video and cyber-physical systems. It highlights the disadvantages of current DL approaches and recommends future research directions for improving ADSs.
