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Author: Abdul Rahman
Publisher: John Wiley & Sons
ISBN: 1394206453
Category : Computers
Languages : en
Pages : 293
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Book Description
A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization's cyber posture with RL and illuminate the most probable adversarial attack paths in your networks. Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You'll also find: A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scaling Comprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph construct Practical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectively Complete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenarios Perfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.
Author: Abdul Rahman
Publisher: John Wiley & Sons
ISBN: 1394206453
Category : Computers
Languages : en
Pages : 293
Get Book Here
Book Description
A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization's cyber posture with RL and illuminate the most probable adversarial attack paths in your networks. Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You'll also find: A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scaling Comprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph construct Practical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectively Complete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenarios Perfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.
Author: Chong Li
Publisher: CRC Press
ISBN: 1351006606
Category : Computers
Languages : en
Pages : 249
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Book Description
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.
Author: Charles A. Kamhoua
Publisher: John Wiley & Sons
ISBN: 1119593360
Category : Technology & Engineering
Languages : en
Pages : 704
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Book Description
An essential guide to the modeling and design techniques for securing systems that utilize the Internet of Things Modeling and Design of Secure Internet of Things offers a guide to the underlying foundations of modeling secure Internet of Things' (IoT) techniques. The contributors—noted experts on the topic—also include information on practical design issues that are relevant for application in the commercial and military domains. They also present several attack surfaces in IoT and secure solutions that need to be developed to reach their full potential. The book offers material on security analysis to help with in understanding and quantifying the impact of the new attack surfaces introduced by IoT deployments. The authors explore a wide range of themes including: modeling techniques to secure IoT, game theoretic models, cyber deception models, moving target defense models, adversarial machine learning models in military and commercial domains, and empirical validation of IoT platforms. This important book: Presents information on game-theory analysis of cyber deception Includes cutting-edge research finding such as IoT in the battlefield, advanced persistent threats, and intelligent and rapid honeynet generation Contains contributions from an international panel of experts Addresses design issues in developing secure IoT including secure SDN-based network orchestration, networked device identity management, multi-domain battlefield settings, and smart cities Written for researchers and experts in computer science and engineering, Modeling and Design of Secure Internet of Things contains expert contributions to provide the most recent modeling and design techniques for securing systems that utilize Internet of Things.
Author: Shishir K. Shandilya
Publisher: Springer Nature
ISBN: 3030193535
Category : Technology & Engineering
Languages : en
Pages : 153
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Book Description
This book contains research contributions from leading cyber security scholars from around the world. The authors provide comprehensive coverage of various cyber security topics, while highlighting recent trends. The book also contains a compendium of definitions and explanations of concepts, processes, acronyms, and comprehensive references on existing literature and research on cyber security and analytics, information sciences, decision systems, digital forensics, and related fields. As a whole, the book is a solid reference for dynamic and innovative research in the field, with a focus on design and development of future-ready cyber security measures. Topics include defenses against ransomware, phishing, malware, botnets, insider threats, and many others.
Author: Charles A. Kamhoua
Publisher: John Wiley & Sons
ISBN: 1119723949
Category : Technology & Engineering
Languages : en
Pages : 546
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Book Description
GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
Author: Jürgen Beyerer
Publisher: Springer
ISBN: 3662584859
Category : Technology & Engineering
Languages : en
Pages : 144
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Book Description
This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Author: Sushil Jajodia
Publisher: Springer Nature
ISBN: 3030307190
Category : Computers
Languages : en
Pages : 270
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Book Description
Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneous functionalities but randomized manifestations. The 10 papers included in this State-of-the Art Survey present recent advances made by a large team of researchers working on the same US Department of Defense Multidisciplinary University Research Initiative (MURI) project during 2013-2019. This project has developed a new class of technologies called Adaptive Cyber Defense (ACD) by building on two active but heretofore separate research areas: Adaptation Techniques (AT) and Adversarial Reasoning (AR). AT methods introduce diversity and uncertainty into networks, applications, and hosts. AR combines machine learning, behavioral science, operations research, control theory, and game theory to address the goal of computing effective strategies in dynamic, adversarial environments.
Author: Csaba Grossi
Publisher: Springer Nature
ISBN: 3031015517
Category : Computers
Languages : en
Pages : 89
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Book Description
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration
Author: Jessica Coates
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110732009
Category : Language Arts & Disciplines
Languages : en
Pages : 558
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Book Description
Information is a critical resource for personal, economic and social development. Libraries and archives are the primary access point to information for individuals and communities with much of the information protected by copyright or licence terms. In this complex legal environment, librarians and information professionals operate at the fulcrum of copyright’s balance, ensuring understanding of and compliance with copyright legislation and enabling access to knowledge in the pursuit of research, education and innovation. This book, produced on behalf of the IFLA Copyright and other Legal Matters (CLM) Advisory Committee, provides basic and advanced information about copyright, outlines limitations and exceptions, discusses communicating with users and highlights emerging copyright issues. The chapters note the significance of the topic; describe salient points of the law and legal concepts; present selected comparisons of approaches around the world; highlight opportunities for reform and advocacy; and help libraries and librarians find their way through the copyright maze.
Author: Qobad Shafiee
Publisher: John Wiley & Sons
ISBN: 1119906202
Category : Technology & Engineering
Languages : en
Pages : 452
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Book Description
Microgrids Presents microgrid methodologies in modeling, stability, and control, supported by real-time simulations and experimental studies Microgrids: Dynamic Modeling, Stability and Control, provides comprehensive coverage of microgrid modeling, stability, and control, alongside new relevant perspectives and research outcomes, with vital information on several microgrid modeling methods, stability analysis methodologies and control synthesis approaches that are supported by real-time simulations and experimental studies for active learning in professionals and students alike. This book is divided into two parts: individual microgrids and interconnected microgrids. Both parts provide individual chapters on modeling, stability, and control, providing comprehensive information on the background, concepts, and architecture, supported by several examples and corresponding source codes/simulation files. Communication based control and cyber security of microgrids are addressed and new outcomes and advances in interconnected microgrids are discussed. Summarizing the outcome of more than 15 years of the authors’ teaching, research, and projects, Microgrids: Dynamic Modeling, Stability and Control covers specific sample topics such as: Microgrid dynamic modeling, covering microgrid components modeling, DC and AC microgrids modeling examples, reduced-order models, and model validation Microgrid stability analysis, covering stability analysis methods, islanded/grid connected/interconnected microgrid stability Microgrids control, covering hierarchical control structure, communication-based control, cyber-resilient control, advanced control theory applications, virtual inertia control and data-driven control Modeling, analysis of stability challenges, and emergency control of large-scale interconnected microgrids Synchronization stability of interconnected microgrids, covering control requirements of synchronous microgrids and inrush power analysis With comprehensive, complete, and accessible coverage of the subject, Microgrids: Dynamic Modeling, Stability and Control is the ideal reference for professionals (engineers, developers) and students working with power/smart grids, renewable energy, and power systems, to enable a more effective use of their microgrids or interconnected microgrids.
