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

Chong Li, Meikang Qiu

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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.

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  • 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,強化學習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人工智能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處理系統日益複雜的問題,也無法自動適應從未遇到過的新情況。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。

特點:
- 介紹強化學習,包括強化學習的高級主題
- 將強化學習應用於物聯網系統和網路安全
- 每章節提供最新的案例和練習
- 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。