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

＂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).＂--

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.