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

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)

「強化學習應用於物聯網系統：附帶網路安全案例研究」一書受到強化學習（RL）和物聯網系統（CPS）領域的最新發展啟發而寫成。強化學習根植於行為心理學，是機器學習的主要分支之一。與其他機器學習算法（如監督學習和非監督學習）不同，RL的關鍵特點是其獨特的學習範式，即試錯法。結合深度神經網絡，深度強化學習變得非常強大，許多複雜的系統可以由AI代理以超人水平自動管理。另一方面，物聯網系統被視為在不久的將來改變我們社會的重要力量。這些例子包括新興的智能建築、智能交通和電力網絡。

然而，傳統的手動編程控制器在物聯網系統中既無法應對系統日益複雜的情況，也無法自動適應從未遇到過的新情境。如何應用現有的深度強化學習算法，或者開發新的強化學習算法以實現實時自適應的物聯網系統仍然是一個未解決的問題。本書旨在通過系統性地介紹強化學習的基礎和算法，並以一個或多個最新的物聯網系統案例來支持每個章節，幫助讀者理解強化學習技術的直觀和實用性。

本書的特點包括：
- 介紹強化學習，包括RL的高級主題
- 將強化學習應用於物聯網系統和網路安全
- 每章包含最新的案例和練習題
- 提供兩個網路安全案例研究

「強化學習應用於物聯網系統：附帶網路安全案例研究」是科學、工程、計算機科學或應用數學領域的研究生或大三/大四本科生的理想教材。對於對網路安全、強化學習和物聯網系統感興趣的研究人員和工程師也很有用。閱讀本書所需的唯一背景知識是基本的微積分和概率論。

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)

Chong Li是Nakamoto & Turing Labs Inc.的共同創辦人。他是Canonchain Network的首席架構師和研究主管。他也是哥倫比亞大學的兼職助理教授。Li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和信息科學學士學位，並在愛荷華州立大學獲得電氣和計算機工程博士學位。

Li博士的研究興趣包括信息理論、機器學習、區塊鏈、網絡控制和通信、編碼理論、5G技術及其後續PHY/MAC設計。Li博士在包括IEEE會議論文集、IEEE信息理論交易、IEEE通信雜誌、Automatica等頂級期刊上發表了許多技術論文。他曾擔任多個國際會議的會議主席和技術計劃委員會成員。他還擔任過許多知名期刊和國際會議的審稿人，包括IEEE信息理論交易、IEEE無線通信交易、ISIT、CDC、ICC、WCNC、Globecom等。他擁有200多項國際和美國專利（已獲得和待批），並獲得了幾項學術獎項，包括聯發科技和吳大猷學者獎、羅森費爾德國際獎學金和愛荷華州立大學研究優秀獎。在高通公司，Li博士在系統設計和幾個新興關鍵技術的標準化方面做出了重大貢獻，包括LTE-D、LTE控制的WiFi和5G。在哥倫比亞大學，他指導研究生課程，如強化學習、區塊鏈技術和凸優化，並在相關領域積極進行研究。最近，Li博士一直致力於基於區塊鏈的地理分佈式共享計算的研究和開發，並在Canonchain負責專利相關業務。

Meikang Qiu在上海交通大學獲得學士和碩士學位，並在德克薩斯大學達拉斯分校獲得計算機科學博士學位。目前，他是哥倫比亞大學的兼職教授和佩斯大學的計算機科學副教授。他是IEEE高級會員和ACM高級會員。他是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、混合內存、異構系統、嵌入式系統、操作系統、優化、智能系統、感測器網絡等。

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