ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning

This publication has 27 references indexed in Scilit:

• Artificial intelligence based optimization with deep learning model for blockchain enabled intrusion detection in CPS environment
  Scientific Reports, 2022
• A lightweight approach for network intrusion detection in industrial cyber-physical systems based on knowledge distillation and deep metric learning
  Expert Systems with Applications, 2022
• A multi‐objective multi‐agent deep reinforcement learning approach to residential appliance scheduling
  IET Smart Grid, 2022
• An intelligent cognitive computing based intrusion detection for industrial cyber-physical systems
  Measurement, 2021
• Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model
  Journal of Parallel and Distributed Computing, 2021
• Policy-based reinforcement learning for time series anomaly detection
  Engineering Applications of Artificial Intelligence, 2020
• Improving adaptive honeypot functionality with efficient reinforcement learning parameters for automated malware
  Journal of Cyber Security Technology, 2018
• Traffic light control using deep policy‐gradient and value‐function‐based reinforcement learning
  IET Intelligent Transport Systems, 2017
• Mastering the game of Go with deep neural networks and tree search
  Nature, 2016
• Human-level control through deep reinforcement learning
  Nature, 2015