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Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents

A paper proposing a policy learning technique for partially observable reinforcement learning agents in autonomous cyber-defense.

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Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents

By Ankita Samaddar, Sandeep Neema, Daniel Balasubramanian, Xenofon KoutsoukosarXiv
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The authors propose an imitation learning-based policy learning technique to predict red agent actions in autonomous cyber-environments. The method is integrated with a neurosymbolic cyber-defense agent and achieves high prediction accuracy across diverse scenarios.

This approach addresses the challenge of partially observable systems, where defender actions are not directly observable.

Abstract

The authors propose an imitation learning-based policy learning technique to predict red agent actions in autonomous cyber-environments. The method is integrated with a neurosymbolic cyber-defense agent and achieves high prediction accuracy across diverse scenarios. This approach addresses the challenge of partially observable systems, where defender actions are not directly observable.

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autonomous cyber-defensereinforcement learningpolicy learning techniqueimitation learningneurosymbolic approachespartially observable systemsAI AgentsAgent MemoryContent EngineeringSemantic Interoperability
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