Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats was developed by a group of leading researchers. It describes the fundamental challenges facing the research community and identifies new promising solution paths. Moving Target Defense which is motivated by the asymmetric costs borne by cyber defenders takes an advantage afforded to attackers and reverses it to advantage defenders. Moving Target Defense is enabled by technical trends in recent years, including virtualization and workload migration on commodity systems, widespread and redundant network connectivity, instruction set and address space layout randomization, just-in-time compilers, among other techniques. However, many challenging research problems remain to be solved, such as the security of virtualization infrastructures, secure and resilient techniques to move systems within a virtualized environment, automatic diversification techniques, automated ways to dynamically change and manage the configurations of systems and networks, quantification of security improvement, potential degradation and more. Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats is designed for advanced -level students and researchers focused on computer science, and as a secondary text book or reference. Professionals working in this field will also find this book valuable.

Our cyber defenses are static and are governed by lengthy processes, e.g., for testing and security patch deployment. Adversaries could plan their attacks carefully over time and launch attacks at cyber speeds at any given moment. We need a new class of defensive strategies that would force adversaries to continually engage in reconnaissance and re-planning of their cyber operations. One such strategy is to present adversaries with a moving target where the attack surface of a system keeps changing. Moving Target Defense II: Application of Game Theory and Adversarial Modeling includes contributions from world experts in the cyber security field. In the first volume of MTD, we presented MTD approaches based on software transformations, and MTD approaches based on network and software stack configurations. In this second volume of MTD, a group of leading researchers describe game theoretic, cyber maneuver, and software transformation approaches for constructing and analyzing MTD systems. Designed as a professional book for practitioners and researchers working in the cyber security field, advanced -level students and researchers focused on computer science will also find this book valuable as a secondary text book or reference.

Our cyber defenses are static and are governed by lengthy processes, e.g., for testing and security patch deployment. Adversaries could plan their attacks carefully over time and launch attacks at cyber speeds at any given moment. We need a new class of defensive strategies that would force adversaries to continually engage in reconnaissance and re-planning of their cyber operations. One such strategy is to present adversaries with a moving target where the attack surface of a system keeps changing. Moving Target Defense II: Application of Game Theory and Adversarial Modeling includes contributions from world experts in the cyber security field. In the first volume of MTD, we presented MTD approaches based on software transformations, and MTD approaches based on network and software stack configurations. In this second volume of MTD, a group of leading researchers describe game theoretic, cyber maneuver, and software transformation approaches for constructing and analyzing MTD systems. Designed as a professional book for practitioners and researchers working in the cyber security field, advanced -level students and researchers focused on computer science will also find this book valuable as a secondary text book or reference.

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.

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.

Discusses virtual network security concepts Considers proactive security using moving target defense Reviews attack representation models based on attack graphs and attack trees Examines service function chaining in virtual networks with security considerations Recognizes machine learning and AI in network security

This book discusses uncertain threats, which are caused by unknown attacks based on unknown vulnerabilities or backdoors in the information system or control devices and software/hardware. Generalized robustness control architecture and the mimic defense mechanisms are presented in this book, which could change “the easy-to-attack and difficult-to-defend game” in cyberspace. The endogenous uncertain effects from the targets of the software/hardware based on this architecture can produce magic “mimic defense fog”, and suppress in a normalized mode random disturbances caused by physical or logic elements, as well as effects of non-probability disturbances brought by uncertain security threats. Although progress has been made in the current security defense theories in cyberspace and various types of security technologies have come into being, the effectiveness of such theories and technologies often depends on the scale of the prior knowledge of the attackers, on the part of the defender and on the acquired real-timing and accuracy regarding the attackers’ behavior features and other information. Hence, there lacks an efficient active defense means to deal with uncertain security threats from the unknown. Even if the bottom-line defense technologies such as encrypted verification are adopted, the security of hardware/software products cannot be quantitatively designed, verified or measured. Due to the “loose coupling” relationship and border defense modes between the defender and the protected target, there exist insurmountable theoretical and technological challenges in the protection of the defender and the target against the utilization of internal vulnerabilities or backdoors, as well as in dealing with attack scenarios based on backdoor-activated collaboration from both inside and outside, no matter how augmented or accumulated protective measures are adopted. Therefore, it is urgent to jump out of the stereotyped thinking based on conventional defense theories and technologies, find new theories and methods to effectively reduce the utilization of vulnerabilities and backdoors of the targets without relying on the priori knowledge and feature information, and to develop new technological means to offset uncertain threats based on unknown vulnerabilities and backdoors from an innovative perspective. This book provides a solution both in theory and engineering implementation to the difficult problem of how to avoid the uncontrollability of product security caused by globalized marketing, COTS and non-trustworthy software/hardware sources. It has been proved that this revolutionary enabling technology has endowed software/hardware products in IT/ICT/CPS with endogenous security functions and has overturned the attack theories and methods based on hardware/software design defects or resident malicious codes. This book is designed for educators, theoretical and technological researchers in cyber security and autonomous control and for business technicians who are engaged in the research on developing a new generation of software/hardware products by using endogenous security enabling technologies and for other product users. Postgraduates in IT/ICT/CPS/ICS will discover that (as long as the law of “structure determines the nature and architecture determines the security is properly used), the problem of software/hardware design defects or malicious code embedding will become the swelling of Achilles in the process of informationization and will no longer haunt Pandora’s box in cyberspace. Security and opening-up, advanced progressiveness and controllability seem to be contradictory, but there can be theoretically and technologically unified solutions to the problem.

The Model Rules of Professional Conduct provides an up-to-date resource for information on legal ethics. Federal, state and local courts in all jurisdictions look to the Rules for guidance in solving lawyer malpractice cases, disciplinary actions, disqualification issues, sanctions questions and much more. In this volume, black-letter Rules of Professional Conduct are followed by numbered Comments that explain each Rule's purpose and provide suggestions for its practical application. The Rules will help you identify proper conduct in a variety of given situations, review those instances where discretionary action is possible, and define the nature of the relationship between you and your clients, colleagues and the courts.

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.

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.