Deep Reinforcement Learning for Anomaly Detection: A Systematic Review-Reference-Cited by-同舟云学术

Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

Published:2022 Issue: Volume:10 Page:124017-124035
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Arshad Kinza¹,Ali Rao Faizan¹^ORCID,Muneer Amgad²^ORCID,Aziz Izzatdin Abdul²^ORCID,Naseer Sheraz³^ORCID,Khan Nabeel Sabir⁴^ORCID,Taib Shakirah Mohd²

Affiliation:

1. Department of Software Engineering, University of Management and Technology, Lahore, Pakistan

2. Department of Computer and Information Sciences, Centre for Research in Data Science (CERDAS), Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

3. Department of Computer Science, University of Engineering and Technology, KSK Campus, Lahore, Pakistan

4. Department of Computer Science, University of Central Punjab, Lahore, Pakistan

Funder

Center for Research in Data Science (CERDAS) and Institute of Autonomous Systems and Autonomous Facilities (IAS), Universiti Teknologi PETRONAS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09956995.pdf?arnumber=9956995

Reference68 articles.

1. A deep reinforcement learning based intrusion detection system (DRL-IDS) for securing wireless sensor networks and Internet of Things;benaddi;Proc 1st Int Conf Wireless Internet,2019

2. A Deep Reinforcement Learning Approach for Anomaly Network Intrusion Detection System

3. Towards Adaptive Anomaly Detection in Buildings with Deep Reinforcement Learning

4. Deep Reinforcement Learning for Real-world Anomaly Detection in Surveillance Videos

5. Learning of binocular fixations using anomaly detection with deep reinforcement learning

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