Reinforcement learning for cyber operations : applications of artificial intelligence for penetration testing
tarafından
 
Rahman, Abdul (Executive), author.

Başlık
Reinforcement learning for cyber operations : applications of artificial intelligence for penetration testing

Yazar
Rahman, Abdul (Executive), author.

ISBN
9781394206476
 
9781394206469
 
9781394206483

Fiziksel Tanımlama
1 online resource (xxx, 256 pages) : illustrations (some color)

İçerik
List of Figures xv -- About the Authors xix -- Foreword xxi -- Preface xxiii -- Acknowledgments xxv -- Acronyms xxvii -- Introduction xxix -- 1 Motivation 1 -- 1.1 Introduction 1 -- 1.1.1 Cyberattack Campaigns via MITRE ATT&CK 4 -- 1.2 Attack Graphs 4 -- 1.3 Cyber Terrain 5 -- 1.4 Penetration Testing 6 -- 1.5 AI Reinforcement Learning Overview 6 -- 1.6 Organization of the Book 8 -- 2 Overview of Penetration Testing 11 -- 2.1 Penetration Testing 11 -- 2.2 Importance of Data 43 -- 2.3 Conclusion 56 -- 3 Reinforcement Learning: Theory and Application 61 -- 3.1 An Introduction to Reinforcement Learning (RL) 61 -- 3.2 RL and Markov Decision Processes 63 -- 3.3 Learnable Functions for Agents 66 -- 3.4 Enter Deep Learning 69 -- 3.5 Q-Learning and Deep Q-Learning 72 -- 3.6 Advantage Actor-Critic (A2C) 78 -- 3.7 Proximal Policy Optimization 83 -- 3.8 Conclusion 85 -- 4 Motivation for Model-driven Penetration Testing 89 -- 4.1 Introduction 89 -- 4.2 Limits of Modern Attack Graphs 91 -- 4.3 RL for Penetration Testing 93 -- 4.4 Modeling MDPs 95 -- 4.5 Conclusion 98 -- 5 Operationalizing RL for Cyber Operations 105 -- 5.1 A High-Level Architecture 105 -- 5.2 Layered Reference Model 107 -- 5.3 Key Challenges for Operationalizing RL 113 -- 5.4 Conclusions 117 -- 6 Toward Practical RL for Pen-Testing 121 -- 6.1 Current Challenges to Practicality 121 -- 6.2 Practical Scalability in RL 130 -- 6.3 Model Realism 136 -- 6.4 Examples of Applications 144 -- 6.5 Realism and Scale 154 -- 7 Putting it Into Practice: RL for Scalable Penetration Testing 161 -- 7.1 Crown Jewels Analysis 161 -- 7.2 Discovering Exfiltration Paths 165 -- 7.3 Discovering Command and Control Channels 171 -- 7.4 Exposing Surveillance Detection Routes 176 -- 7.5 Enhanced Exfiltration Path Analysis 183 -- 8 Using and Extending These Models 193 -- 8.1 Supplementing Penetration Testing 193 -- 8.2 Risk Scoring 199 -- 8.3 Further Modeling 201 -- 8.4 Generalization 214 -- 9 Model-driven Penetration Testing in Practice 225 -- 9.1 Recap 225 -- 9.2 The Case for Model-driven Cyber Detections 231 -- References 246 -- A Appendix 251 -- Index 253.

Özet
"Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge)."-- Provided by publisher.

Notlar
John Wiley and Sons

Konu Terimleri
Reinforcement learning.
 
Penetration testing (Computer security)
 
Apprentissage par renforcement (Intelligence artificielle)
 
Tests d'intrusion.

Elektronik Erişim
https://onlinelibrary.wiley.com/doi/book/10.1002/9781394206483


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Çevrimiçi KütüphaneE-Kitap599600-1001Q325.6 .R34 2025Wiley E-Kitap Koleksiyonu