Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Arshad, Kinza, Ali, Rao Faizan, Muneer, Amgad, Aziz, Izzatdin Abdul, Naseer, Sheraz, Khan, Nabeel Sabir, Taib, Shakirah Mohd
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Anomaly detection; Data models; Datasets; Deep learning; Deep Reinforcement Learning; Literature reviews; Machine learning; Machine learning algorithms; Performance assessment; Reinforcement learning; Supervised learning; systematic review; Systematics

Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep R...