Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case StudiesКНИГИ » ПРОГРАММИНГ
Название: Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies Автор: Chong Li, Meikang Qiu Издательство: Chapman and Hall/CRC Год: 2019 Страниц: 257 Язык: английский Формат: pdf (true), djvu Размер: 10.19 MB
Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids.
However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system, nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques.
Features:
Introduces reinforcement learning, including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapter Provides two cybersecurity case studies
Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.
Preface xiii Author Bios xvii Section I Introduction Chapter 1 Overview of Reinforcement Learning 3 1.1 OVERVIEW OF REINFORCEMENT LEARNING 4 1.1.1 Introduction 4 1.1.2 Comparison with Other Machine Learning Methods 6 1.1.3 An Example of Reinforcement Learning 9 1.1.4 Applications of Reinforcement Learning 11 1.2 HISTORY OF REINFORCEMENT LEARNING 14 1.2.1 Traditional Reinforcement Learning 14 1.2.2 Deep Reinforcement Learning 17 1.3 SIMULATION TOOLKITS FOR REINFORCEMENT LEARNING 18 Chapter 2 Overview of Cyber-Physical Systems and Cybersecurity 21 2.1 INTRODUCTION 22 2.2 EXAMPLES OF CYBER-PHYSICAL SYSTEMS RESEARCH 24 2.3 CYBERSECURITY THREATS 29 Section II Reinforcement Learning for Cyber-Physical Systems Chapter 3 Reinforcement Learning Problems 43 Chapter 4 Model-Based Reinforcement Learning 69 Chapter 5 Model-Free Reinforcement Learning 93 Chapter 6 Deep Reinforcement Learning 125 Section III Case Studies Chapter 7 Reinforcement Learning for Cybersecurity 155 Chapter 8 Case Study: Online Cyber-Attack Detection in Smart Grid 169 Chapter 9 Case Study: Defeat Man-in-the-Middle Attack 189 Bibliography 205 Index 227
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