Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters

no code implementations • 9 Feb 2024 • Edward S. Harake, Joseph R. Linzey, Cheng Jiang, Rushikesh S. Joshi, Mark M. Zaki, Jaes C. Jones, Siri S. Khalsa, John H. Lee, Zachary Wilseck, Jacob R. Joseph, Todd C. Hollon, Paul Park

SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists.

Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs

no code implementations • 28 Jan 2022 • Tyler Cody, Abdul Rahman, Christopher Redino, Lanxiao Huang, Ryan Clark, Akshay Kakkar, Deepak Kushwaha, Paul Park, Peter Beling, Edward Bowen

Reinforcement learning (RL), in conjunction with attack graphs and cyber terrain, are used to develop reward and state associated with determination of optimal paths for exfiltration of data in enterprise networks.

reinforcement-learning Reinforcement Learning (RL)

Crown Jewels Analysis using Reinforcement Learning with Attack Graphs

no code implementations • 20 Aug 2021 • Rohit Gangupantulu, Tyler Cody, Abdul Rahman, Christopher Redino, Ryan Clark, Paul Park

Cyber attacks pose existential threats to nations and enterprises.

reinforcement-learning Reinforcement Learning (RL)

Using Cyber Terrain in Reinforcement Learning for Penetration Testing

no code implementations • 16 Aug 2021 • Rohit Gangupantulu, Tyler Cody, Paul Park, Abdul Rahman, Logan Eisenbeiser, Dan Radke, Ryan Clark

Reinforcement learning (RL) has been applied to attack graphs for penetration testing, however, trained agents do not reflect reality because the attack graphs lack operational nuances typically captured within the intelligence preparation of the battlefield (IPB) that include notions of (cyber) terrain.

reinforcement-learning Reinforcement Learning (RL)