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Autonomous Cyber Deception by Ehab Al-Shaer,Jinpeng Wei,Kevin W. Hamlen,Cliff Wang Pdf
This textbook surveys the knowledge base in automated and resilient cyber deception. It features four major parts: cyber deception reasoning frameworks, dynamic decision-making for cyber deception, network-based deception, and malware deception. An important distinguishing characteristic of this book is its inclusion of student exercises at the end of each chapter. Exercises include technical problems, short-answer discussion questions, or hands-on lab exercises, organized at a range of difficulties from easy to advanced,. This is a useful textbook for a wide range of classes and degree levels within the security arena and other related topics. It’s also suitable for researchers and practitioners with a variety of cyber security backgrounds from novice to experienced.
Adaptive Autonomous Secure Cyber Systems by Sushil Jajodia,George Cybenko,V.S. Subrahmanian,Vipin Swarup,Cliff Wang,Michael Wellman Pdf
This book explores fundamental scientific problems essential for autonomous cyber defense. Specific areas include: Game and control theory-based moving target defenses (MTDs) and adaptive cyber defenses (ACDs) for fully autonomous cyber operations; The extent to which autonomous cyber systems can be designed and operated in a framework that is significantly different from the human-based systems we now operate; On-line learning algorithms, including deep recurrent networks and reinforcement learning, for the kinds of situation awareness and decisions that autonomous cyber systems will require; Human understanding and control of highly distributed autonomous cyber defenses; Quantitative performance metrics for the above so that autonomous cyber defensive agents can reason about the situation and appropriate responses as well as allowing humans to assess and improve the autonomous system. This book establishes scientific foundations for adaptive autonomous cyber systems and ultimately brings about a more secure and reliable Internet. The recent advances in adaptive cyber defense (ACD) have developed a range of new ACD techniques and methodologies for reasoning in an adaptive environment. Autonomy in physical and cyber systems promises to revolutionize cyber operations. The ability of autonomous systems to execute at scales, scopes, and tempos exceeding those of humans and human-controlled systems will introduce entirely new types of cyber defense strategies and tactics, especially in highly contested physical and cyber environments. The development and automation of cyber strategies that are responsive to autonomous adversaries pose basic new technical challenges for cyber-security. This book targets cyber-security professionals and researchers (industry, governments, and military). Advanced-level students in computer science and information systems will also find this book useful as a secondary textbook.
Cyber Deception by Sushil Jajodia,V.S. Subrahmanian,Vipin Swarup,Cliff Wang Pdf
This edited volume features a wide spectrum of the latest computer science research relating to cyber deception. Specifically, it features work from the areas of artificial intelligence, game theory, programming languages, graph theory, and more. The work presented in this book highlights the complex and multi-facted aspects of cyber deception, identifies the new scientific problems that will emerge in the domain as a result of the complexity, and presents novel approaches to these problems. This book can be used as a text for a graduate-level survey/seminar course on cutting-edge computer science research relating to cyber-security, or as a supplemental text for a regular graduate-level course on cyber-security.
Cyber Deception by Tiffany Bao,Milind Tambe,Cliff Wang Pdf
This book introduces recent research results for cyber deception, a promising field for proactive cyber defense. The beauty and challenge of cyber deception is that it is an interdisciplinary research field requiring study from techniques and strategies to human aspects. This book covers a wide variety of cyber deception research, including game theory, artificial intelligence, cognitive science, and deception-related technology. Specifically, this book addresses three core elements regarding cyber deception: Understanding human’s cognitive behaviors in decoyed network scenarios Developing effective deceptive strategies based on human’s behaviors Designing deceptive techniques that supports the enforcement of deceptive strategies The research introduced in this book identifies the scientific challenges, highlights the complexity and inspires the future research of cyber deception. Researchers working in cybersecurity and advanced-level computer science students focused on cybersecurity will find this book useful as a reference. This book also targets professionals working in cybersecurity. Chapter 'Using Amnesia to Detect Credential Database Breaches' and Chapter 'Deceiving ML-Based Friend-or-Foe Identification for Executables' are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Game Theory for Cyber Deception by Jeffrey Pawlick,Quanyan Zhu Pdf
This book introduces game theory as a means to conceptualize, model, and analyze cyber deception. Drawing upon a collection of deception research from the past 10 years, the authors develop a taxonomy of six species of defensive cyber deception. Three of these six species are highlighted in the context of emerging problems such as privacy against ubiquitous tracking in the Internet of things (IoT), dynamic honeynets for the observation of advanced persistent threats (APTs), and active defense against physical denial-of-service (PDoS) attacks. Because of its uniquely thorough treatment of cyber deception, this book will serve as a timely contribution and valuable resource in this active field. The opening chapters introduce both cybersecurity in a manner suitable for game theorists and game theory as appropriate for cybersecurity professionals. Chapter Four then guides readers through the specific field of defensive cyber deception. A key feature of the remaining chapters is the development of a signaling game model for the species of leaky deception featured in honeypots and honeyfiles. This model is expanded to study interactions between multiple agents with varying abilities to detect deception. Game Theory for Cyber Deception will appeal to advanced undergraduates, graduate students, and researchers interested in applying game theory to cybersecurity. It will also be of value to researchers and professionals working on cybersecurity who seek an introduction to game theory.
Cyber Denial, Deception and Counter Deception by Kristin E. Heckman,Frank J. Stech,Roshan K. Thomas,Ben Schmoker,Alexander W. Tsow Pdf
This book presents the first reference exposition of the Cyber-Deception Chain: a flexible planning and execution framework for creating tactical, operational, or strategic deceptions. This methodology bridges the gap between the current uncoordinated patchwork of tactical denial and deception (D&D) techniques and their orchestration in service of an organization’s mission. Concepts for cyber- D&D planning operations and management are detailed within the larger organizational, business, and cyber defense context. It examines the necessity of a comprehensive, active cyber denial scheme. The authors explain the organizational implications of integrating D&D with a legacy cyber strategy, and discuss trade-offs, maturity models, and lifecycle management. Chapters present the primary challenges in using deception as part of a security strategy, and guides users through the steps to overcome common obstacles. Both revealing and concealing fact and fiction have a critical role in securing private information. Detailed case studies are included. Cyber Denial, Deception and Counter Deception is designed as a reference for professionals, researchers and government employees working in cybersecurity. Advanced-level students in computer science focused on security will also find this book useful as a reference or secondary text book.
Modeling and Design of Secure Internet of Things by Charles A. Kamhoua,Laurent L. Njilla,Alexander Kott,Sachin Shetty Pdf
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.
Autonomous Intelligent Cyber Defense Agent (AICA) by Alexander Kott Pdf
This book offers a structured overview and a comprehensive guide to the emerging field of Autonomous Intelligent Cyber Defense Agents (AICA). The book discusses the current technical issues in autonomous cyber defense and offers information on practical design approaches. The material is presented in a way that is accessible to non-specialists, with tutorial information provided in the initial chapters and as needed throughout the book. The reader is provided with clear and comprehensive background and reference material for each aspect of AICA. Today’s cyber defense tools are mostly watchers. They are not active doers. They do little to plan and execute responses to attacks, and they don’t plan and execute recovery activities. Response and recovery – core elements of cyber resilience – are left to human cyber analysts, incident responders and system administrators. This is about to change. The authors advocate this vision, provide detailed guide to how such a vision can be realized in practice, and its current state of the art. This book also covers key topics relevant to the field, including functional requirements and alternative architectures of AICA, how it perceives and understands threats and the overall situation, how it plans and executes response and recovery, how it survives threats, and how human operators deploy and control AICA. Additionally, this book covers issues of testing, risk, and policy pertinent to AICA, and provides a roadmap towards future R&D in this field. This book targets researchers and advanced students in the field of cyber defense and resilience. Professionals working in this field as well as developers of practical products for cyber autonomy will also want to purchase this book.
Game Theory and Machine Learning for Cyber Security by Charles A. Kamhoua,Christopher D. Kiekintveld,Fei Fang,Quanyan Zhu Pdf
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.
Game Theory and Machine Learning for Cyber Security by Charles A. Kamhoua,Christopher D. Kiekintveld,Fei Fang,Quanyan Zhu Pdf
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.
Cross-Layer Design for Secure and Resilient Cyber-Physical Systems by Quanyan Zhu,Zhiheng Xu Pdf
This book introduces a cross-layer design to achieve security and resilience for CPSs (Cyber-Physical Systems). The authors interconnect various technical tools and methods to capture the different properties between cyber and physical layers. Part II of this book bridges the gap between cryptography and control-theoretic tools. It develops a bespoke crypto-control framework to address security and resiliency in control and estimation problems where the outsourcing of computations is possible. Part III of this book bridges the gap between game theory and control theory and develops interdependent impact-aware security defense strategies and cyber-aware resilient control strategies. With the rapid development of smart cities, there is a growing need to integrate the physical systems, ranging from large-scale infrastructures to small embedded systems, with networked communications. The integration of the physical and cyber systems forms Cyber-Physical Systems (CPSs), enabling the use of digital information and control technologies to improve the monitoring, operation, and planning of the systems. Despite these advantages, they are vulnerable to cyber-physical attacks, which aim to damage the physical layer through the cyber network. This book also uses case studies from autonomous systems, communication-based train control systems, cyber manufacturing, and robotic systems to illustrate the proposed methodologies. These case studies aim to motivate readers to adopt a cross-layer system perspective toward security and resilience issues of large and complex systems and develop domain-specific solutions to address CPS challenges. A comprehensive suite of solutions to a broad range of technical challenges in secure and resilient control systems are described in this book (many of the findings in this book are useful to anyone working in cybersecurity). Researchers, professors, and advanced-level students working in computer science and engineering will find this book useful as a reference or secondary text. Industry professionals and military workers interested in cybersecurity will also want to purchase this book.
Adversarial Machine Learning by Aneesh Sreevallabh Chivukula,Xinghao Yang,Bo Liu,Wei Liu,Wanlei Zhou Pdf
A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.
Decision and Game Theory for Security by Quanyan Zhu,John S. Baras,Radha Poovendran,Juntao Chen Pdf
This book constitutes the refereed proceedings of the 11th International Conference on Decision and Game Theory for Security, GameSec 2020,held in College Park, MD, USA, in October 2020. Due to COVID-19 pandemic the conference was held virtually The 21 full papers presented together with 2 short papers were carefully reviewed and selected from 29 submissions. The papers focus on machine learning and security; cyber deception; cyber-physical systems security; security of network systems; theoretic foundations of security games; emerging topics.
A penetrating look at the dark side of emerging AI technologies In The Language of Deception: Weaponizing Next Generation AI, artificial intelligence and cybersecurity veteran Justin Hutchens delivers an incisive and penetrating look at how contemporary and future AI can and will be weaponized for malicious and adversarial purposes. In the book, you will explore multiple foundational concepts to include the history of social engineering and social robotics, the psychology of deception, considerations of machine sentience and consciousness, and the history of how technology has been weaponized in the past. From these foundations, the author examines topics related to the emerging risks of advanced AI technologies, to include: The use of Large Language Models (LLMs) for social manipulation, disinformation, psychological operations, deception and fraud The implementation of LLMs to construct fully autonomous social engineering systems for targeted attacks or for mass manipulation at scale The technical use of LLMs and the underlying transformer architecture for use in technical weapons systems to include advanced next-generation malware, physical robotics, and even autonomous munition systems Speculative future risks such as the alignment problem, disembodiment attacks, and flash wars. Perfect for tech enthusiasts, cybersecurity specialists, and AI and machine learning professionals, The Language of Deception is an insightful and timely take on an increasingly essential subject.
