This book describes a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. It begins by introducing basic concepts on game theory, machine learning, cyber security and cyber deception. Further chapters bring together the best researchers and practitioners in cyber security to share their latest research contributions in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. The book provides expert insights on applying these new methods to address cyber autonomy, 5G security, blockchain technology, attack graphs, sensor manipulation, fault injection, moving target defense, Cyber-Physical Systems (CPS), Internet-of-Battle- Things (IoBT), multi-domain battle. The book closes by summarizing ongoing research topics in cyber security and points to open issues and future research challenges.

Charles A. Kamhoua, PhD, is a researcher at the United States Army Research Laboratory’s Network Security Branch. He is co-editor of Assured Cloud Computing (2018) and Blockchain for Distributed Systems Security (2019), and Modeling and Design of Secure Internet of Things (2020).

Christopher D. Kiekintveld, PhD, is Associate Professor at the University of Texas at El Paso. He is Director of Graduate Programs with the Computer Science Department.

Fei Fang, PhD, is Assistant Professor in the Institute for Software Research at the School of Computer Science at Carnegie Mellon University.

Quanyan Zhu, PhD, is Associate Professor in the Department of Electrical and Computer Engineering at New York University.

Editor biographies

Contributors

Foreword

Preface

Chapter 1:           Introduction

Christopher D. Kiekintveld, Charles A. Kamhoua, Fei Fang, Quanyan Zhu

Part 1:   Game Theory for Cyber Deception

Chapter 2:           Introduction to Game Theory

Fei Fang, Shutian Liu, Anjon Basak, Quanyan Zhu, Christopher Kiekintveld, Charles A. Kamhoua

Chapter 3:           Scalable Algorithms for Identifying Stealthy Attackers in a Game Theoretic Framework Using Deception

Anjon Basak, Charles Kamhoua, Sridhar Venkatesan, Marcus Gutierrez, Ahmed H. Anwar, Christopher Kiekintveld

Chapter 4:           Honeypot Allocation Game over Attack Graphs for Cyber Deception

Ahmed H. Anwar, Charles Kamhoua, Nandi Leslie, Christopher Kiekintveld

Chapter 5:           Evaluating Adaptive Deception Strategies for Cyber Defense with Human Experimentation

Palvi Aggarwal, Marcus Gutierrez, Christopher Kiekintveld, Branislav Bosansky, Cleotilde Gonzalez

Chapter 6:           A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception

Jie Fu, Abhishek N. Kulkarni

Part 2:   Game Theory for Cyber Security

Chapter 7:           Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization

Muhammed O. Sayin, Dinuka Sahabandu, Muhammad Aneeq uz Zaman, Radha Poovendran, Tamer Başar

Chapter 8:           Sensor Manipulation Games in Cyber Security

João P. Hespanha

Chapter 9:           Adversarial Gaussian Process Regression in Sensor Networks

Yi Li, Xenofon Koutsoukos, Yevgeniy Vorobeychik

Chapter 10:        Moving Target Defense Games for Cyber Security: Theory and Applications Abdelrahman Eldosouky, Shamik Sengupta

Chapter 11:        Continuous Authentication Security Games

Serkan Saritas, Ezzeldin Shereen, Henrik Sandberg, Gyorgy Dan

Chapter 12:        Cyber Autonomy in Software Security: Techniques and Tactics

Tiffany Bao, Yan Shoshitaishvili

Part 3:   Adversarial Machine Learning for Cyber Security

Chapter 13:        A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications

Yan Zhou, Murat Kantarcioglu, Bowei Xi

Chapter 14:        Adversarial Machine Learning in 5G Communications Security

Yalin Sagduyu, Tugba Erpek, Yi Shi

Chapter 15:        Machine Learning in the Hands of a Malicious Adversary: A Near Future If Not Reality Keywhan Chung, Xiao Li, Peicheng Tang, Zeran Zhu, Zbigniew T. Kalbarczyk, Thenkurussi Kesavadas, Ravishankar K. Iyer

Chapter 16:        Trinity: Trust, Resilience and Interpretability of Machine Learning Models

Susmit Jha, Anirban Roy, Brian Jalaian, Gunjan Verma

Part 4:   Generative Models for Cyber Security

Chapter 17:        Evading Machine Learning based Network Intrusion Detection Systems with GANs Bolor-Erdene Zolbayar, Ryan Sheatsley, Patrick McDaniel, Mike Weisman

Chapter 18:        Concealment Charm (ConcealGAN): Automatic Generation of Steganographic Text using Generative Models to Bypass Censorship

Nurpeiis Baimukan, Quanyan Zhu

Part 5:   Reinforcement Learning for Cyber Security

Chapter 19:        Manipulating Reinforcement Learning: Stealthy Attacks on Cost Signals

Yunhan Huang, Quanyan Zhu

Chapter 20:        Resource-Aware Intrusion Response based on Deep Reinforcement Learning for Software-Defined Internet-of-Battle-Things

Seunghyun Yoon, Jin-Hee Cho, Gaurav Dixit, Ing-Ray Chen

Part 6:   Other Machine Learning approach to Cyber Security

Chapter 21:        Smart Internet Probing: Scanning Using Adaptive Machine Learning

Armin Sarabi, Kun Jin, Mingyan Liu

Chapter 22:        Semi-automated Parameterization of a Probabilistic Model using Logistic Regression - A Tutorial

Stefan Rass, Sandra König, Stefan Schauer

Chapter 23:        Resilient Distributed Adaptive Cyber-Defense using Blockchain

George Cybenko, Roger A. Hallman

Chapter 24:        Summary and Future Work

Quanyan Zhu, Fei Fang