Reliability is one of the major design criteria in Cyber-Physical Systems (CPSs). This is because of the existence of some critical applications in CPSs and their failure is catastrophic. Therefore, employing strong error detection and correction mechanisms in CPSs is inevitable. CPSs are composed of a variety of units, including sensors, networks, and microcontrollers. Each of these units is probable to be in a faulty state at any time and the occurred fault can result in erroneous output. The fault may cause the units of CPS to malfunction and eventually crash. Traditional fault-tolerant approaches include redundancy time, hardware, information, and/or software. However, these approaches impose significant overheads besides their low error coverage, which limits their applicability. In addition, the interval between error occurrence and detection is too long in these approaches. In this paper, based on Deep Reinforcement Learning (DRL), a new error detection approach is proposed that not only detects errors with high accuracy but also can perform error detection at the moment due to very low inference time. The proposed approach can categorize different types of errors from normal data and predict whether the system will fail. The evaluation results illustrate that the proposed approach has improved more than 2x in terms of accuracy and more than 5x in terms of inference time compared to other approaches.
Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；State Grid XiXian New Area Power Supply Company（国家电网西安新区供电公司）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Energy Technology, Aalborg University（奥尔堡大学能源技术研究所）；Illinois Institute of Technology（伊利诺伊理工大学）
Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning
Command and Control Engineering College, Army Engineering University of PLA（中国人民解放军陆军工程大学指挥控制工程学院）；troop of PLA（中国人民解放军31436部队）；Academy of Military Science（军事科学院）
Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Department of Electrical and Computer Engineering, Royal Military College of Canada（加拿大皇家军事学院电子与计算机工程系）；Defence Research and Development Canada（加拿大国防研究与发展加拿大）；Department of Computer Engineering and Software Engineering, Polytechnique Montréal（蒙特利尔理工学院计算机工程与软件工程系）
Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Electrical and Computer Engineering Department, Royal Military College of Canada（加拿大皇家军事学院电子与计算机工程系）；Defence Research and Development Canada（加拿大国防研究发展署）
本文提出在Cyber Operations Research Gym中利用可微分智能体间学习算法训练防御智能体，使其在部分可观测网络环境中学习低成本通信与防御战术策略，从而提升自主网络防御的协同决策能力。
KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning
Command and Control Engineering College, Army Engineering University of PLA（中国人民解放军陆军工程大学指挥控制工程学院）；troop of PLA（中国人民解放军31436部队）；Academy of Military Science（军事科学院）
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
Cyber-Defence Campus & Eastern Switzerland University of Applied Sciences（网络安全校园与东瑞士应用科学大学）；Institute for Computational Engineering（计算工程研究所）；armasuisse Science and Technology（armasuisse科技）
Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems
Departmento de Ingenier\'ia El\'ectrica, Universidad de Concepci\'on, Concepci\'on, Chile (e-mail: )；Department of Electrical \& Computer Engineering, University of Alberta, Edmonton, T6G 1H9, Alberta, AB, Canada (e-mail: )；Department of Chemical；Biological Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada ( )；Department of Chemical \& Materials Engineering, University of Alberta, Edmonton, T6G 1H9, Alberta, AB, Canada (e-mail: )
SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）；Cardiff University（卡迪夫大学）；KU Leuven（鲁汶大学）；Devotion AI Labs（Devotion AI 实验室）
Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems
State Key Laboratory of Robotics and Intelligent Systems（机器人与智能系统国家重点实验室）；Shenyang Institute of Automation（沈阳自动化研究所）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning
The University of Tokyo（东京大学）；Japan Society for the Promotion of Science（日本学术振兴会）；Tianjin University（天津大学）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所（AIST））
Adversarial Online Learning with Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity
Crown Jewels Analysis using Reinforcement Learning with Attack Graphs
Deloitte Consulting, LLC（德勤咨询有限责任公司）；Hume Center for National Security and Technology, Virginia Polytechnic University（弗吉尼亚理工大学休姆国家安全与技术中心）；Deloitte & Touche LLP（德勤会计师事务所）
