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

Rahman, Abdul; Redino, Christopher; Nandakumar, Dhruv; Cody, Tyler; Shetty, Sachin; Radke, Dan

 
9781394206452: Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing
Couverture rigide
ISBN 10 : 1394206453 ISBN 13 : 9781394206452
Editeur : Wiley-IEEE Press, 2024

Synopsis

"Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge)."--

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À propos de l?auteur

Dr. Abdul Rahman holds PhDs in physics, math, information technology-cybersecurity and has expertise in cybersecurity, big data, blockchain, and analytics (AI, ML).

Dr. Christopher Redino holds a PhD in theoretical physics and has extensive data science experience in every part of the AI / ML lifecycle.

Mr. Dhruv Nandakumar has extensive data science expertise in deep learning.

Dr. Tyler Cody is an Assistant Research Professor at the Virginia Tech National Security Institute.

Dr. Sachin Shetty 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.

Mr. Dan Radke is an Information Security professional with extensive experience in both offensive and defensive cybersecurity.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Wiley-IEEE Press
Date d'édition
2024
Langue
anglais
ISBN 10 
1394206453
ISBN 13 
9781394206452
Reliure
Relié
Nombre de pages
288
Coordonnées du fabricant
non disponible
Personne responsable
non disponible