Reinforcement Learning for Cyber Operations

Reinforcement Learning for Cyber Operations

  • Abdul Rahman
  • Christopher Redino
  • Sachin Shetty
  • Dhruv Nandakumar
  • Tyler Cody
  • Dan Radke
Publisher:John Wiley & SonsISBN 13: 9781394206452ISBN 10: 1394206453

Paperback & Hardcover deals ―

Amazon IndiaGOFlipkart GOSnapdealGOSapnaOnlineGOJain Book AgencyGOBooks Wagon₹8,076Book ChorGOCrosswordGODC BooksGO

e-book & Audiobook deals ―

Amazon India GOGoogle Play Books GOAudible GO

* Price may vary from time to time.

* GO = We're not able to fetch the price (please check manually visiting the website).

Know about the book -

Reinforcement Learning for Cyber Operations is written by Abdul Rahman and published by John Wiley & Sons. It's available with International Standard Book Number or ISBN identification 1394206453 (ISBN 10) and 9781394206452 (ISBN 13).

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.