Reinforcement Learning for Cyber-Physical Systems: With Cybersecurity Case Studies
暫譯: 強化學習在網路物理系統中的應用：包含網路安全案例研究

Li, Chong, Qiu, Meikang

Reinforcement Learning for Cyber-Physical Systems: With Cybersecurity Case Studies

相關主題

商品描述

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.

 

 

商品描述(中文翻譯)

《強化學習於網路物理系統：結合網路安全案例研究》受到強化學習（Reinforcement Learning, RL）和網路物理系統（Cyber-Physical Systems, CPS）領域近期發展的啟發。強化學習根植於行為心理學，是機器學習的主要分支之一。與其他機器學習演算法（如監督式學習和非監督式學習）不同，強化學習的關鍵特徵在於其獨特的學習範式，即試錯法。結合深度神經網絡，深度強化學習（Deep RL）變得如此強大，以至於許多複雜系統可以由人工智慧代理以超人類的水平自動管理。另一方面，網路物理系統被預期將在不久的將來徹底改變我們的社會。這類例子包括新興的智慧建築、智能交通和電力網。

然而，傳統的手動編程控制器在網路物理系統中無法處理系統日益增加的複雜性，也無法自動適應其從未遇到過的新情況。如何應用現有的深度強化學習演算法，或開發新的強化學習演算法以實現實時自適應的網路物理系統，仍然是一個未解決的問題。本書旨在通過系統地介紹強化學習的基礎和演算法，建立兩個領域之間的聯繫，每個演算法都由一個或幾個最先進的網路物理系統範例支持，以幫助讀者理解強化學習技術的直覺和實用性。

**特色**

- 介紹強化學習，包括強化學習中的進階主題
- 將強化學習應用於網路物理系統和網路安全
- 每章包含最先進的範例和練習
- 提供兩個網路安全案例研究

《強化學習於網路物理系統：結合網路安全案例研究》是科學、工程、計算機科學或應用數學領域研究生或大學三、四年級學生的理想教材。對於對網路安全、強化學習和網路物理系統感興趣的研究人員和工程師也將非常有用。欣賞本書所需的唯一背景知識是基本的微積分和機率論知識。

作者簡介

Chong Li is co-founder of Nakamoto \& Turing Labs Inc. He is Chief Architect and Head of Research at Canonchain Network. He is also an adjunct assistant professor at Columbia University. Dr. Li was a staff research engineer in the department of corporate R&D at Qualcomm Technologies. He received a B.E. in Electronic Engineering and Information Science from Harbin Institute of Technology and a Ph.D in Electrical and Computer Engineering from Iowa State University.　

 

Dr. Li's research interests include information theory, machine learning, blockchain, networked control and communications, coding theory, PHY/MAC design for 5G technology and beyond. Dr. Li has published many technical papers in top-ranked journals, including Proceedings of the IEEE, IEEE Transactions on Information Theory, IEEE Communications Magazine, Automatica, etc. He has served as session chair and technical program committee for a number of international conferences. He has also served as reviewer for many prestigious journals and international conferences, including IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communication, ISIT, CDC, ICC, WCNC, Globecom, etc. He holds 200+ international and U.S. patents (granted and pending) and received several academic awards including the MediaTek Inc. and Wu Ta You Scholar Award, the Rosenfeld International Scholarship and Iowa State Research Excellent Award. At Qualcomm, Dr. Li significantly contributed to the systems design and the standardization of several emerging key technologies, including LTE-D, LTE-controlled WiFi and 5G. At Columbia University, he has been instructing graduate-level courses, such as reinforcement learning, blockchain technology and convex optimization, and actively conducting research in the related field. 　Recently, Dr. Li has been driving the research and development of blockchain-based geo-distributed shared computing, and managing the patent-related business at Canonchain.　

 

Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received Ph.D. degree of Computer Science from University of Texas at Dallas. Currently, he is an Adjunct Professor at Columbia University and Associate Professor of Computer Science at Pace University. He is an IEEE Senior member and ACM Senior member. He is the Chair of IEEE Smart Computing Technical Committee. His research interests include cyber security, cloud computing, big data storage, hybrid memory, heterogeneous systems, embedded systems, operating systems, optimization, intelligent systems, sensor networks, etc.

 

 

作者簡介(中文翻譯)

李崇是Nakamoto & Turing Labs Inc.的共同創辦人。他是Canonchain Network的首席架構師及研究主管。他同時也是哥倫比亞大學的兼任助理教授。李博士曾在高通技術公司的企業研發部門擔任研究工程師。他在哈爾濱工業大學獲得電子工程與信息科學的學士學位，並在愛荷華州立大學獲得電氣與計算機工程的博士學位。

李博士的研究興趣包括信息理論、機器學習、區塊鏈、網絡控制與通信、編碼理論、5G技術及其後續技術的物理層/媒介存取控制設計等。李博士在多個頂尖期刊上發表了許多技術論文，包括《IEEE會議錄》、《IEEE信息理論期刊》、《IEEE通信雜誌》、《自動化》等。他曾擔任多個國際會議的會議主席和技術程序委員會成員，並為許多知名期刊和國際會議擔任審稿人，包括《IEEE信息理論期刊》、《IEEE無線通信期刊》、《ISIT》、《CDC》、《ICC》、《WCNC》、《Globecom》等。他擁有200多項國際及美國專利（已授權及待授權），並獲得多項學術獎項，包括聯發科技及吳大猷學者獎、羅森費爾德國際獎學金及愛荷華州立大學研究卓越獎。在高通，李博士對多項新興關鍵技術的系統設計和標準化做出了重要貢獻，包括LTE-D、LTE控制的WiFi和5G。在哥倫比亞大學，他教授研究生課程，如強化學習、區塊鏈技術和凸優化，並積極進行相關領域的研究。最近，李博士推動基於區塊鏈的地理分佈共享計算的研究與開發，並管理Canonchain的專利相關業務。

邱美康在上海交通大學獲得BE和ME學位，並在德克薩斯大學達拉斯分校獲得計算機科學的博士學位。目前，他是哥倫比亞大學的兼任教授及佩斯大學的計算機科學副教授。他是IEEE資深會員和ACM資深會員，並擔任IEEE智能計算技術委員會的主席。他的研究興趣包括網絡安全、雲計算、大數據存儲、混合記憶體、異構系統、嵌入式系統、操作系統、優化、智能系統、傳感器網絡等。

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