LEADER 03525nam 22005653 450 001 9911019975303321 005 20250102080301.0 010 $a9781394206476 010 $a139420647X 010 $a9781394206483 010 $a1394206488 010 $a9781394206469 010 $a1394206461 035 $a(CKB)37082735600041 035 $a(MiAaPQ)EBC31867357 035 $a(Au-PeEL)EBL31867357 035 $a(Perlego)4772731 035 $a(EXLCZ)9937082735600041 100 $a20250102d2025 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReinforcement Learning for Cyber Operations $eApplications of Artificial Intelligence for Penetration Testing 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$dİ2025. 215 $a1 online resource (289 pages) 311 08$a9781394206452 311 08$a1394206453 330 8 $aA 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. 606 $aReinforcement learning 606 $aPenetration testing (Computer security) 615 0$aReinforcement learning. 615 0$aPenetration testing (Computer security) 676 $a006.3/1 700 $aRahman$b Abdul$01837502 701 $aRedino$b Christopher$01837503 701 $aNandakumar$b Dhruv$01837504 701 $aCody$b Tyler$01403193 701 $aShetty$b Sachin$0871287 701 $aRadke$b Dan$01837505 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019975303321 996 $aReinforcement Learning for Cyber Operations$94416242 997 $aUNINA