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Reinforcement learning for cyber-phy...

Li, Chong, (1985-)

Reinforcement learning for cyber-physical systems with cybersecurity case studies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement learning for cyber-physical systems with cybersecurity case studies /Chong Li, Meikang Qiu.
作者:	Li, Chong,
其他作者:	Qiu, Meikang,
面頁冊數:	1 online resource.
標題:	Reinforcement learning.
電子資源:	https://www.taylorfrancis.com/books/9781351006620
電子資源:	http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ISBN:	9781351006613$q(electronic bk.)

Reinforcement learning for cyber-physical systems with cybersecurity case studies /
Li, Chong,1985-

Reinforcement learning for cyber-physical systems with cybersecurity case studies /Chong Li, Meikang Qiu. - 1 online resource.

Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; Preface; Author Bios; Section I: Introduction; Chapter 1 Overview of Reinforcement Learning; 1.1 OVERVIEW OF REINFORCEMENT LEARNING; 1.1.1 Introduction; 1.1.2 Comparison with Other Machine Learning Methods; 1.1.3 An Example of Reinforcement Learning; 1.1.4 Applications of Reinforcement Learning; 1.2 HISTORY OF REINFORCEMENT LEARNING; 1.2.1 Traditional Reinforcement Learning; 1.2.2 Deep Reinforcement Learning; 1.3 SIMULATION TOOLKITS FOR REINFORCEMENT LEARNING; 1.4 REMARKS

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.

ISBN: 9781351006613$q(electronic bk.)

Standard No.: 10.1201/9781351006620doiSubjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6 / .L53 2019eb

Dewey Class. No.: 006.3/1

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505 0 $a Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; Preface; Author Bios; Section I: Introduction; Chapter 1 Overview of Reinforcement Learning; 1.1 OVERVIEW OF REINFORCEMENT LEARNING; 1.1.1 Introduction; 1.1.2 Comparison with Other Machine Learning Methods; 1.1.3 An Example of Reinforcement Learning; 1.1.4 Applications of Reinforcement Learning; 1.2 HISTORY OF REINFORCEMENT LEARNING; 1.2.1 Traditional Reinforcement Learning; 1.2.2 Deep Reinforcement Learning; 1.3 SIMULATION TOOLKITS FOR REINFORCEMENT LEARNING; 1.4 REMARKS
505 8 $a Chapter 2 Overview of Cyber-Physical Systems and Cybersecurity2.1 INTRODUCTION; 2.2 EXAMPLES OF CYBER-PHYSICALSYSTEMS RESEARCH; 2.2.1 Resource Allocation; 2.2.2 Data Transmission and Management; 2.2.3 Energy Control; 2.2.4 Model-Based Software Design; 2.3 CYBERSECURITY THREATS; 2.3.1 Adversaries in Cybersecurity; 2.3.2 Objectives of Cybersecurity; 2.3.2.1 Confidentiality; 2.3.2.2 Integrity; 2.3.2.3 Availability; 2.3.2.4 Authenticity; 2.4 REMARKS; 2.5 EXERCISES; Section II: Reinforcement Learning for Cyber-Physical Systems; Chapter 3 Reinforcement Learning Problems
505 8 $a 5.4.2 Double Q-Learning5.5 REMARKS; 5.6 EXERCISES; Chapter 6 Deep Reinforcement Learning; 6.1 INTRODUCTION TO DEEP RL; 6.2 DEEP NEURAL NETWORKS; 6.2.1 Convolutional Neural Networks; 6.2.2 Recurrent Neural Networks; 6.3 DEEP LEARNING TO VALUE FUNCTIONS; 6.3.1 DQN; 6.3.1.1 Example; 6.4 DEEP LEARNING TO POLICY FUNCTIONS; 6.4.1 DDPG; 6.4.2 A3C; 6.4.2.1 Example; 6.5 DEEP LEARNING TO RL MODEL; 6.6 DRL COMPUTATION EFFICIENCY; 6.7 REMARKS; 6.8 EXERCISES; Section III: Case Studies; Chapter 7 Reinforcement Learning for Cybersecurity; 7.1 TRADITIONAL CYBERSECURITY METHODS
520 $a 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.
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650 0 $a Computer security. $3 184416
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