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.

This monograph is the second of a two-part survey and analysis of the state of the art in secure processor systems, with a specific focus on remote software attestation and software isolation. The first part established the taxonomy and prerequisite concepts relevant to an examination of the state of the art in trusted remote computation: attested software isolation containers (enclaves). This second part extends Part I's description of Intel's Software Guard Extensions (SGX), an available and documented enclave-capable system, with a rigorous security analysis of SGX as a system for trusted remote computation. This part documents the authors' concerns over the shortcomings of SGX as a secure system and introduces the MIT Sanctum processor developed by the authors: a system designed to offer stronger security guarantees, lend itself better to analysis and formal verification, and offer a more straightforward and complete threat model than the Intel system, all with an equivalent programming model. This two-part work advocates a principled, transparent, and well scrutinized approach to system design, and argues that practical guarantees of privacy and integrity for remote computation are achievable at a reasonable design cost and performance overhead. See also: Secure Processors Part I: Background, Taxonomy for Secure Enclaves and Intel SGX Architecture (ISBN 978-1-68083-300-3). Part I of this survey establishes the taxonomy and prerequisite concepts relevant to an examination of the state of the art in trusted remote computation: attested software isolation containers (enclaves).

This monograph is the first in a two-part survey and analysis of the state of the art in secure processor systems, with a specific focus on remote software attestation and software isolation. It first examines the relevant concepts in computer architecture and cryptography, and then surveys attack vectors and existing processor systems claiming security for remote computation and/or software isolation. It examines, in detail, the modern isolation container (enclave) primitive as a means to minimize trusted software given practical trusted hardware and reasonable performance overhead. Specifically, this work examines the programming model and software design considerations of Intel's Software Guard Extensions (SGX), as it is an available and documented enclave-capable system. This work advocates a principled, transparent, and well-scrutinized approach to secure system design, and argues that practical guarantees of privacy and integrity for remote computation are achievable at a reasonable design cost and performance overhead. See also: Secure Processors Part II: Intel SGX Security Analysis and MIT Sanctum Architecture Part II (ISBN 978-1-68083-302-7). Part II of this survey a deep dive into the implementation and security evaluation of two modern enclave-capable secure processor systems: SGX and MIT's Sanctum. The complex but insufficient threat model employed by SGX motivates Sanctum, which achieves stronger security guarantees under software attacks with an equivalent programming model.

Lattice-based cryptography is the use of conjectured hard problems on point lattices in Rn as the foundation for secure cryptographic systems. Attractive features of lattice cryptography include apparent resistance to quantum attacks (in contrast with most number-theoretic cryptography), high asymptotic efficiency and parallelism, security under worst-case intractability assumptions, and solutions to long-standing open problems in cryptography. This monograph surveys most of the major developments in lattice cryptography over the past ten years. The main focus is on the foundational short integer solution (SIS) and learning with errors (LWE) problems (and their more efficient ring-based variants), their provable hardness assuming the worst-case intractability of standard lattice problems, and their many cryptographic applications.