Reinforcement Learning for Cyber Operations
Wiley-IEEE Press (Verlag)
978-1-394-20645-2 (ISBN)
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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.
Abdul Rahman, PhD, is an Associate Vice President with the AI Center of Excellence in Deloitte's Risk and Financial practice. Chris Redino is a Master Data Scientist with the AI Center of Excellence within Deloitte's Risk and Financial advisory practice. He has extensive experience in every part of the machine learning lifecycle. Dhruv Nandakumar is a Lead Data Scientist with the AI Center of Excellence at Deloitte. Tyler Cody, PhD, is an Assistant Research Professor at the Virginia Tech National Security Institute. Sachin Shetty, PhD, is a Professor in the Electrical and Computer Engineering Department at Old Dominion University and the Executive Director of the Center for Secure and Intelligent Critical Systems at the Virginia Modeling, Analysis and Simulation Center. Dan Radke is a member of the Deloitte Advisory Cyber Security Risk Services team.
| Erscheint lt. Verlag | 25.3.2025 |
|---|---|
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Kryptologie |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-394-20645-3 / 1394206453 |
| ISBN-13 | 978-1-394-20645-2 / 9781394206452 |
| Zustand | Neuware |
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