Buch, Englisch, 448 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 769 g
Buch, Englisch, 448 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 769 g
ISBN: 978-1-394-21538-6
Verlag: John Wiley & Sons
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Preface xv
Editor Biographies xvii
1 Introduction 1
Alexander Kott
1.1 Cyber Resilience and Cybersecurity 1
1.2 Autonomy and Cyber Resilience 3
1.3 Autonomous Actions 4
1.4 Approaches to Implementing Autonomous Cyber Resilience 5
1.5 Benefits and Risks of Autonomous Cyber Resilience 8
1.6 The Preview of the Book 9
Part 1 Cyber Resilience 13
2 Game-theoretic Foundations for Cyber Resilience Against Deceptive Information Attacks in Intelligent Transportation Systems 15
Ya-Ting Yang and Quanyan Zhu
2.1 Introduction 15
2.2 Deceptive Information Attacks 19
2.3 Cross-layer Resilience 29
2.4 Case Study 35
2.5 Conclusion and Discussion 43
3 CYBER-MIRA: Cyber Mission Impact Resilience Assessment Framework for Tactical Mission Systems 53
Ashrith Reddy Thukkaraju, Han Jun Yoon, Shou Matsumoto, Jair Feldens Ferrari, Donghwan Lee, Myung Kil Ahn, Paulo Costa, and Jin-Hee Cho
3.1 Introduction 53
3.2 Related Work 56
3.3 System Model 59
3.4 CYBER-MIRA Framework 64
3.5 Limitations 86
3.6 Conclusion and Future Work 86
4 Modeling Autonomous Network Resilience in Adversarial Environments Using Machine Learning and Topological Data Analysis 91
Nandi O. Leslie
4.1 Introduction 91
4.2 TDA Concepts 93
4.3 Network Resilience Modeling 97
4.4 Conclusion 101
5 Game-theoretic Frameworks for Zero-trust Authentication in Autonomous Cyber Resilience 105
Yunfei Ge and Quanyan Zhu
5.1 Introduction 105
5.2 From Traditional Security to Zero Trust 108
5.3 Trust Evaluation Design 110
5.4 Policy Engine Design 116
5.5 Zero Trust for Cyber Resilience 118
5.6 Strategic Zero-trust Implementation 123
5.7 Conclusion 132
6 Cyber Insurance for Cyber Resilience 139
Shutian Liu and Quanyan Zhu
6.1 Introduction 139
6.2 Attack Models and Insured Targets 144
6.3 Defense Mechanisms and Residual Risks 149
6.4 Insurer's Observations and the P-A Model 155
6.5 Modeling of Risk Preferences 158
6.6 Insurance Design with Preference Manipulation 162
6.7 Dynamic Insurances 164
6.8 Regulations on Cyber Insurance 166
6.9 Conclusion 170
7 Enhancing Cyber Resiliency: Assessing the Effectiveness of Deploying Honeypots in Different Network Topologies 183
7.1 Introduction 183
7.2 Task Description 185
7.3 A Cognitive Model of Attackers in HackIT 193
7.4 Discussion 199
Part 2 Resilient Machine Learning 203
8 Computational Game Theory for Security 205
Yevgeniy Vorobeychik
8.1 Introduction 205
8.2 Stackelberg Games 206
8.3 Stackelberg Security Games 209
8.4 Security Games on Networks 212
8.5 Stochastic Stackelberg Games and Adversarial Patrolling 215
8.6 Conclusion 221
9 Privacy and Robustness Trade-offs of Artificial Intelligence Models with Federated Learning 225
Kemal Davaslioglu, Yi Shi, and Yalin E. Sagduyu
9.1 Introduction 225
9.2 Model Inversion Attacks 229
9.3 Membership Inference Attacks 243
9.4 Federated Learning 264
9.5 Discussion 275
9.6 Conclusions 276
9.7 Acknowledgment 277
10 Resilient Deep Neural Network Random Ensemble Against Adversarial Attacks 281
Kirsen Sullivan, Yitao Li, Charles A. Kamhoua, and Bowei xi
10.1 Introduction 281
10.2 Data and Bootstrapped CNNs 284
10.3 Bootstrapped Distributions 290
10.4 Randomized DNN Ensembles with Gaussian Random Weights 300
10.5 Ensemble Experiment Results 302
10.6 Conclusion 311
Part 3 Game Theory for Network Resilience 317
11 Poisoning Attack and Defense Game for Federated Learning in Resilient NextG Networks 319
Yalin E. Sagduyu, Tugba Erpek, and Yi Shi
11.1 Introduction 319
11.2 Federated Learning for Distributed Spectrum Monitoring 323
11.3 Attack and Defense Mechanisms for Resilient Federated Learning 325
11.4 Poisoning Attack–Defense Game for Two Clients 330
11.5 Poisoning Attack–Defense Game for More than Two Clients 337
11.6 Future Research Directions 338
11.7 Conclusion 341
12 Self-adapting Quantum Network Provisioning Using Game Theory 347
Stefan Rass, Miralem Mehic, Sandra König, Stefan Schauer, and Miroslav Voznak
12.1 Introduction 347
12.2 Basics of Quantum Networks 348
12.3 Game Theory to Orchestrate Cryptography 349
12.4 Self-adaption of QKD Devices to Environmental Conditions 361
12.5 Adapting the Level of Service to Traffic Changes 369
12.6 Adapting the Network Topology 372
12.7 Conclusions, Outlook, and QKD in Today's Networks 376
13 Conclusion and Future Works 383
Quanyan Zhu
13.1 Overview 383
13.2 Summary 384
13.3 Future Directions: Charting the Path Toward Autonomous Cyber Resilience 396
13.4 Concluding Remarks 404
Index 407
