What question did this study set out to answer?

This research aims to develop a versatile blue agent capable of defending against various red agents using a combined learning approach.

May 9, 2025Journal of Cybersecurity and PrivacyOpen Access

Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent

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This research aims to develop a versatile blue agent capable of defending against various red agents using a combined learning approach.
Developed a framework integrating reinforcement learning to train blue agents against specific red agents in distinct networking environments.
Utilized supervised learning to create a generic blue agent drawing from multiple RL-trained agents.
Incorporated a variational autoencoder to enhance feature learning and generalization of the blue agent.
The framework successfully trained a generic blue agent capable of defending against different red agents across diverse network topologies.
Demonstrated improved performance over existing methods based on extensive empirical evaluation.
Incorporating a variational autoencoder resulted in further performance enhancements, focusing on high-level behavioral features.

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Implication

Randomized trial demonstrates a framework for training a versatile blue agent against diverse red agents, indicating potential for enhanced cybersecurity.

Cite This Study

Farooq et al. (2025) studied this question.

synapsesocial.com/papers/6a20798404d2adee4bf49150 https://doi.org/10.3390/jcp5020023

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Implication

Randomized trial demonstrates a framework for training a versatile blue agent against diverse red agents, indicating potential for enhanced cybersecurity.

Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent

Key Points

Discussion

Implication

Cite This Study

Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent

Key Points

Discussion

Implication

Cite This Study

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