00898nam0-22003131i-450 99000710348040332120190211161910.00-19-511782-4000710348FED01000710348(Aleph)000710348FED0100071034820020708d2000----km-y0itay50------baengUSy-------001yyContinenthal shelf limitsthe scientific and legal interfaceedited by Peter J. Cook, Chris M. CarletonOxfordOxford University Pressc2000XIV, 363 p.24 cm341.44821itaCarleton,Chris M.Cook,Peter J.ITUNINARICAUNIMARCBK990007103480403321X G 34343505*FGBCFGBCContinenthal shelf limits705317UNINA03525nam 22005653 450 991101997530332120250102080301.09781394206476139420647X9781394206483139420648897813942064691394206461(CKB)37082735600041(MiAaPQ)EBC31867357(Au-PeEL)EBL31867357(Perlego)4772731(EXLCZ)993708273560004120250102d2025 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierReinforcement Learning for Cyber Operations Applications of Artificial Intelligence for Penetration Testing1st ed.Newark :John Wiley & Sons, Incorporated,2025.©2025.1 online resource (289 pages)9781394206452 1394206453 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. Reinforcement learningPenetration testing (Computer security)Reinforcement learning.Penetration testing (Computer security)006.3/1Rahman Abdul1837502Redino Christopher1837503Nandakumar Dhruv1837504Cody Tyler1403193Shetty Sachin871287Radke Dan1837505MiAaPQMiAaPQMiAaPQBOOK9911019975303321Reinforcement Learning for Cyber Operations4416242UNINA