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Full Description
Authoritative and highly comprehensive resource on the latest research and strategies to develop cyber resilience in any network system
Autonomous Cyber Resilience presents key research contributions in the fields of cyber resilience, resilient machine learning, and game theory for network security. It introduces basic concepts on resilience assessment framework, human robot teaming, zero-trust cyber resilience, the Stackelberg network game, and adversarial machine learning. The book describes a comprehensive suite of solutions for a broad range of technical challenges in autonomous cyber resilience, examines network robustness, planning, learning, and self-adaptation in a dynamic and uncertain environment and provides a joint analysis of cyber resilience and machine learning resilience.
The book gathers experts in this emerging area of research to share their latest contributions in federated learning, resilient deep neural networks, topological data analysis, and effective deployment of honeypots, with valuable insights on applying these new methods to address cyber autonomy, network intrusion detection, and NextG communication systems. Additional chapters summarize ongoing research topics in cyber security and point to open issues and future research challenges and opportunities for academia and industry.
Autonomous Cyber Resilience includes information on:
Hypergraphs as a tool to move beyond basic pairwise relations and interactions to accurately model higher order interactions between groups of agents
Settings where multiple, distributed, and collaborative bots involved in an attack can make the impact of vulnerabilities more severe
The Resilience Index, the percentage of Monte Carlo simulations where mission essential functions perform below the acceptable threshold
Eigenvector centrality, a metric that takes into account not just the centrality (degree) of a node but also its power
Providing an extensive set of techniques to meet a diverse array of obstacles in the field, Autonomous Cyber Resilience is essential reading for researchers, students, and experts in the fields of computer science and engineering, along with industry and military professionals involved in projects related to cybersecurity.
Contents
Chapter 1:Introduction Part 1: Cyber Resilience
Chapter 2 Game-Theoretic Foundations for Cyber Resilience Against Deceptive Information Attacks in Intelligent Transportation Systems, Ya-Ting Yang and Quanyan Zhu
Chapter 3 CYBER-MIRA: Cyber Mission Impact Resilience Assessment Framework for Tactical Machine Learning-based Systems, Ashrith Reddy Thukkaraju, Han Joon Yoon, Shou Matsumoto, Jair Feldens Ferrari, Donghwan Lee, Myung Kil Ahn, Paulo Costa, and Jin-Hee Cho
Chapter 4 Modeling Autonomous Network Resilience in Adversarial Environments Using Machine Learning and Topological Data Analysis, Nandi O. Leslie
Chapter 5 Zero-Trust Cyber Resilience, Yunfei Ge and Quanyan Zhu
Chapter 6 Cyber Insurance for Cyber Resilience, Shutian Liu and Quanyan Zhu
Chapter 7 Effectiveness of Deploying Honeypots in Different Network Topologies, Palvi Aggarwal, Yinuo Du, Kuldeep Singh, Shashank Uttrani, Varun Dutt, Cleotilde Gonzalez
Part 2: Resilient Machine Learning
Chapter 8 Towards the Science of Threat Modeling in Adversarial Machine Learning, Yevgeniy Vorobeychik
Chapter 9 Privacy and Robustness Trade-offs of Artificial Intelligence Models with Federated Learning, Kemal Davaslioglu, Yi Shi, and Yalin E. Sagduyu
Chapter 10 Resilient Deep Neural Network Random Ensemble Against Adversarial Attacks, Kirsen Sullivan, Yitao Li, Charles Kamhoua and Bowei Xi
Part 3: Game Theory for Network Resilience
Chapter 11 A Game Theoretic Formulation of Adversarial Attacks and Defenses in NextG Communication Systems, Yalin E. Sagduyu, Tugba Erpek, Yi Shi
Chapter 12 Self-Adapting Quantum Network Provisioning, Stefan Rass, Miralem Mehic, Sandra König, Stefan Schauer, Miroslav Voznak
Chapter 13 A Stackelberg Network Game, Ziyuan Huang and Mingyan Liu
