Reinforcement Learning for Cyber Operations : Applications of Artificial Intelligence for Penetration Testing

• Autore: Rahman Abdul
• Pubblicazione: Wiley, 2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (289 pages)
• Disciplina: 006.3/1
• Soggetto topico: Reinforcement learning; Penetration testing (Computer security)
• Altri autori: RedinoChristopher NandakumarDhruv CodyTyler ShettySachin RadkeDan
• Nota di contenuto: Acronyms Introduction 1 Motivation 2 Overview of Penetration Testing 3 Reinforcement Learning: Theory and Application 4 Motivation for Model-driven Penetration Testing 5 Operationalizing RL for Cyber Operations 6 Toward Practical RL for Pen-Testing 7 Putting it Into Practice: RL for Scalable Penetration Testing 8 Using and Extending These Models 9 Model-driven Penetration Testing in Practice A Appendix End User License Agreement
• Sommario/riassunto: A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization's cyber posture with RL and illuminate the most probable adversarial attack paths in your networks. Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You'll also find: * A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scaling * Comprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph construct * Practical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectively * Complete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenarios Perfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.
• Titolo autorizzato: Reinforcement Learning for Cyber Operations
• ISBN: 9781394206476; 139420647X; 9781394206483; 1394206488; 9781394206469; 1394206461
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911019975303321
• Lo trovi qui: Univ. Federico II