Designing Efficient Anomaly Detection Systems Using Deep Learning Techniques

Purpose 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). Design/methodology/approach 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. Findings This study identifies the drawbacks and limitations of current anomaly detection techniques and presents several DL-based taxonomies and performance metrics for detecting anomalies. Research limitations/implications The paper discusses the limitations and challenges in existing DL-based anomaly detection techniques, providing insights into areas that require further research. Practical implications The study provides practical guidelines for developing and implementing DL-based ADSs across various domains. Social implications Improved ADSs can enhance security, safety and efficiency in medical, image and video and cyber-physical systems, benefiting society as a whole. Originality/value 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.