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 agentsSettings where multiple, distributed, and collaborative bots involved in an attack can make the impact of vulnerabilities more severeThe Resilience Index, the percentage of Monte Carlo simulations where mission essential functions perform below the acceptable thresholdEigenvector centrality, a metric that takes into account not just the centrality (degree) of a node but also its powerProviding 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.

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.

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 agentsSettings where multiple, distributed, and collaborative bots involved in an attack can make the impact of vulnerabilities more severeThe Resilience Index, the percentage of Monte Carlo simulations where mission essential functions perform below the acceptable thresholdEigenvector centrality, a metric that takes into account not just the centrality (degree) of a node but also its powerProviding 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.

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.