Game theory and machine learning for cyber security / / editors, Charles A. Kamhoua [et al.]

• Pubblicazione: Hoboken, NJ : , : John Wiley & Sons, Inc., , [2021]; ©2021
• Descrizione fisica: 1 online resource (547 pages)
• Disciplina: 005.8
• Soggetto topico: Computer networks - Security measures; Game theory; Machine learning
• Persona (resp. second.): KamhouaCharles A.
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- Contributors -- Foreword -- Preface -- Chapter 1 Introduction -- 1.1 Artificial Intelligence and Cybersecurity -- 1.1.1 Game Theory for Cybersecurity -- 1.1.2 Machine Learning for Cybersecurity -- 1.2 Overview -- References -- Part I Game Theory for Cyber Deception -- Chapter 2 Introduction to Game Theory -- 2.1 Overview -- 2.2 Example Two‐Player Zero‐Sum Games -- 2.3 Normal‐Form Games -- 2.3.1 Solution Concepts -- 2.4 Extensive‐Form Games -- 2.4.1 Solution Concepts -- 2.5 Stackelberg Game -- 2.5.1 Solution Concept -- 2.5.2 Stackelberg Security Games -- 2.5.3 Applications in Cybersecurity -- 2.6 Repeated Games -- 2.6.1 Solution Concepts -- 2.6.2 Applications in Cybersecurity -- 2.7 Bayesian Games -- 2.7.1 Solution Concepts -- 2.7.2 Applications in Cybersecurity -- 2.8 Stochastic Games -- 2.8.1 Solution Concepts -- 2.8.2 Applications in Cybersecurity -- References -- Chapter 3 Scalable Algorithms for Identifying Stealthy Attackers in a Game‐Theoretic Framework Using Deception -- 3.1 Introduction -- 3.2 Background -- 3.3 Case Studies -- 3.3.1 Case Study 1: Attackers with Same Exploits but Different Goals -- 3.3.2 Case Study 2: Attackers with Shared Exploits and Different Goals -- 3.3.3 Case Study 3: Attackers with Shared Exploits but Same Goals -- 3.4 Game Model -- 3.5 Defender Decision Making -- 3.6 Attacker Decision Making -- 3.7 Simulation Results -- 3.8 Scalability -- 3.8.1 Heuristics -- 3.9 Evaluation of Heuristics -- 3.10 Conclusions and Future Direction -- References -- Chapter 4 Honeypot Allocation Games over Attack Graphs for Cyber Deception -- 4.1 Introduction -- 4.2 System and Game Model -- 4.2.1 Attack Graph -- 4.2.2 General Game Formulation -- 4.2.2.1 Defender Action -- 4.2.2.2 Attacker Action -- 4.2.3 Reward Function -- 4.2.4 Mixed Strategy. ; 4.2.5 System Parameters -- 4.3 Allocating ℓ Honeypots Model -- 4.3.1 The Algorithm -- 4.4 Dynamic Honeypot Allocation -- 4.4.1 Mixed Strategy, State Evolution, and Objective Function -- 4.4.2 Q‐Minmax Algorithm -- 4.5 Numerical Results -- 4.6 Conclusion and Future Work -- Acknowledgment -- References -- Chapter 5 Evaluating Adaptive Deception Strategies for Cyber Defense with Human Adversaries -- 5.1 Introduction -- 5.1.1 HoneyGame: An Abstract Interactive Game to Study Deceptive Cyber Defense -- 5.2 An Ecology of Defense Algorithms -- 5.2.1 Static Pure Defender -- 5.2.2 Static Equilibrium Defender -- 5.2.3 Learning with Linear Rewards (LLR) -- 5.2.4 Best Response with Thompson sampling (BR‐TS) -- 5.2.5 Probabilistic Best Response with Thompson Sampling (PBR‐TS) -- 5.2.6 Follow the Regularized Leader (FTRL) -- 5.3 Experiments -- 5.3.1 Measures -- 5.4 Experiment 1 -- 5.4.1 Participants -- 5.4.2 Procedure -- 5.4.3 Results -- 5.4.3.1 Average Rewards -- 5.4.3.2 Attacks on Honeypots -- 5.4.3.3 Switching Behavior -- 5.4.3.4 Attack Distribution -- 5.5 Experiment 2 -- 5.5.1 Participants -- 5.5.2 Results -- 5.5.2.1 Average Rewards -- 5.5.2.2 Attacks on Honeypots -- 5.5.2.3 Switching Behavior -- 5.5.2.4 Attack Distribution -- 5.6 Towards Adaptive and Personalized Defense -- 5.7 Conclusions -- Acknowledgements -- References -- Chapter 6 A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception -- 6.1 Introduction -- 6.2 Attack‐Defend Games on Graph -- 6.2.1 Game Arena -- 6.2.2 Specifying the Security Properties in LTL -- 6.3 Hypergames on Graphs -- 6.4 Synthesis of Provably Secure Defense Strategies Using Hypergames on Graphs -- 6.4.1 Synthesis of Reactive Defense Strategies -- 6.4.2 Synthesis of Reactive Defense Strategies with Cyber Deception -- 6.5 Case Study -- 6.6 Conclusion -- References. ; Part II Game Theory for Cyber Security -- Chapter 7 Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization -- 7.1 Introduction -- 7.1.1 Need for Cohesive Detection -- 7.1.2 Need for Strategic Detection -- 7.1.3 Minimax Detection (MAD) -- 7.2 Problem Formulation -- 7.2.1 System Model -- 7.2.2 Defense Model -- 7.2.3 Threat Model -- 7.2.4 Game Model -- 7.3 Main Result -- 7.3.1 Complexity Analysis -- 7.4 Illustrative Examples -- 7.5 Conclusion -- Acknowledgements -- References -- Chapter 8 Sensor Manipulation Games in Cyber Security -- 8.1 Introduction -- 8.2 Measurement Manipulation Games -- 8.2.1 Saddle‐Point Equilibria -- 8.2.2 Approximate Saddle‐Point Equilibrium -- 8.3 Sensor‐Reveal Games -- 8.3.1 Nash Equilibria -- 8.4 Conclusions and Future Work -- References -- Chapter 9 Adversarial Gaussian Process Regression in Sensor Networks -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Anomaly Detection with Gaussian Process Regression -- 9.4 Stealthy Attacks on Gaussian Process Anomaly Detection -- 9.5 The Resilient Anomaly Detection System -- 9.5.1 Resilient Anomaly Detection as a Stackelberg Game -- 9.5.2 Computing an Approximately Optimal Defense -- 9.6 Experiments -- 9.7 Conclusions -- References -- Chapter 10 Moving Target Defense Games for Cyber Security: Theory and Applications -- 10.1 Introduction -- 10.2 Moving Target Defense Theory -- 10.2.1 Game Theory for MTD -- 10.3 Single‐Controller Stochastic Games for Moving Target Defense -- 10.3.1 Stochastic Games -- 10.3.2 Single‐Controller Stochastic Games -- 10.3.2.1 Numerical Example -- 10.4 A Case Study for Applying Single‐Controller Stochastic Games in MTD The case study presented in this section is based on the work in Eldosouky et al. (). -- 10.4.1 Equilibrium Strategy Determination -- 10.4.2 Simulation Results and Analysis -- 10.5 Moving Target Defense Applications. ; 10.5.1 Internet of Things (IoT) Applications -- 10.5.2 Machine Learning Applications -- 10.5.3 Prospective MTD Applications -- 10.6 Conclusions -- References -- Chapter 11 Continuous Authentication Security Games -- 11.1 Introduction -- 11.2 Background and Related Work -- 11.3 Problem Formulation -- 11.3.1 User Behavior -- 11.3.2 Intrusion Detection System Model -- 11.3.3 Model of Continuous Authentication -- 11.3.4 System States without an Attacker -- 11.3.5 Attack Model -- 11.3.5.1 Listening (l(t)& -- equals -- r, a(t)& -- equals -- 0) -- 11.3.5.2 Attacking (l(t)& -- equals -- 0, a(t)& -- equals -- r) -- 11.3.5.3 Waiting (l(t)& -- equals -- 0, a(t)& -- equals -- 0) -- 11.3.6 Continuous Authentication Game -- 11.4 Optimal Attack Strategy under Asymmetric Information -- 11.4.1 MDP Formulation -- 11.4.1.1 Waiting (l(t)& -- equals -- 0, a(t)& -- equals -- 0) -- 11.4.1.2 Listening (l(t)& -- equals -- r, a(t)& -- equals -- 0) -- 11.4.1.3 Attacking (l(t)& -- equals -- 0, a(t)& -- equals -- r) -- 11.4.2 Optimality of the Threshold Policy -- 11.4.2.1 Optimality of Listening -- 11.4.2.2 Optimality of Attacking -- 11.5 Optimal Defense Strategy -- 11.5.1 Expected Defender Utility -- 11.5.2 Analysis without an Attacker -- 11.5.3 Analysis with an Attacker -- 11.6 Numerical Results -- 11.7 Conclusion and Discussion -- References -- Chapter 12 Cyber Autonomy in Software Security: Techniques and Tactics -- 12.1 Introduction -- 12.2 Background -- 12.3 Related Work -- 12.4 Model Setup -- 12.5 Techniques -- 12.6 Tactics -- 12.6.1 Model Parameters -- 12.6.2 Formalization -- 12.6.3 Finding Equilibriums -- 12.6.4 Algorithm -- 12.7 Case Study -- 12.8 Discussion -- 12.9 Conclusion -- References -- Part III Adversarial Machine Learning for Cyber Security. ; Chapter 13 A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications -- 13.1 Introduction to Game Theoretic Adversarial Machine Learning -- 13.2 Adversarial Learning Problem Definition -- 13.3 Game Theory in Adversarial Machine Learning -- 13.3.1 Simultaneous Games -- 13.3.1.1 Zero Sum Games -- 13.3.1.2 Nash Equilibrium Games -- 13.3.2 Sequential Games -- 13.4 Simultaneous Zero‐sum Games in Real Applications -- 13.4.1 Adversarial Attack Models -- 13.4.1.1 Free‐Range Attack -- 13.4.1.2 Restrained Attack -- 13.4.2 Adversarial SVM Learning -- 13.4.2.1 AD‐SVM Against Free‐range Attack Model -- 13.4.2.2 AD‐SVM Against Restrained Attack Model -- 13.4.3 Experiment -- 13.4.3.1 Attack Simulation -- 13.4.3.2 Experimental Results -- 13.4.3.3 A Few Words on Setting Cf, Cξ, and Cδ -- 13.4.4 Remark -- 13.5 Nested Bayesian Stackelberg Games -- 13.5.1 Adversarial Learning -- 13.5.2 A Single Leader Single Follower Stackelberg Game -- 13.5.3 Learning Models and Adversary Types -- 13.5.3.1 Learning Models -- 13.5.3.2 Adversary Types -- 13.5.3.3 Setting Payoff Matrices for the Single Leader Multiple‐followers Game -- 13.5.4 A Single Leader Multi‐followers Stackelberg Game -- 13.5.5 Experiments -- 13.5.5.1 Artificial Datasets -- 13.5.5.2 Real Datasets -- 13.5.6 Remark -- 13.6 Further Discussions -- Acknowledgements -- References -- Chapter 14 Adversarial Machine Learning for 5G Communications Security -- 14.1 Introduction -- 14.2 Adversarial Machine Learning -- 14.3 Adversarial Machine Learning in Wireless Communications -- 14.3.1 Wireless Attacks Built Upon Adversarial Machine Learning -- 14.3.2 Domain‐specific Challenges for Adversarial Machine Learning in Wireless Communications -- 14.3.3 Defense Schemes Against Adversarial Machine Learning -- 14.4 Adversarial Machine Learning in 5G Communications. ; 14.4.1 Scenario 1-Adversarial Attack on 5G Spectrum Sharing.
• Sommario/riassunto: "Cyber security is a serious concern to our economic prosperity and national security. Despite an increased investment in cyber defense, cyber-attackers are becoming more creative and sophisticated. This exposes the need for a more rigorous approach to cyber security, including methods from artificial intelligence including computational game theory and machine learning. Recent advances in adversarial machine learning are promising to make artificial intelligence (AI) algorithms more robust to deception and intelligent manipulation. However, they are still vulnerable to adversarial inputs, data poisoning, model stealing and evasion attacks. The above challenges and the high risk and consequence of cyber-attacks drive the need to accelerate basic research on cyber security"-- Provided by publisher
• Titolo autorizzato: Game theory and machine learning for cyber security
• ISBN: 1-119-72394-9; 1-119-72395-7; 1-119-72391-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910555189803321
• Lo trovi qui: Univ. Federico II