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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding.

Zhen Wang1,2, Yongjie Wang1,2, Xinli Xiong1,2

  • 1College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.

Entropy (Basel, Switzerland)
|June 26, 2025
PubMed
Summary

This study introduces a novel approach for autonomous cyber defense (ACD) by combining graph embedding and reinforcement learning. The method enhances decision-making by improving network topology awareness, leading to more effective cyber defense strategies.

Keywords:
autonomous cyber defensegraph embeddingintelligent decision-makingreinforcement learning

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Traditional autonomous cyber defense (ACD) methods struggle with complex network topologies and interdependencies, hindering the identification of critical nodes and attack paths.
  • Effective ACD requires advanced decision-making that can dynamically adapt to evolving cyber threats and network structures.
  • Current methods often face perceptual bottlenecks in high-dimensional, sparse network environments.

Purpose of the Study:

  • To propose an enhanced decision-making method for autonomous cyber defense (ACD) by integrating graph embedding with reinforcement learning.
  • To improve the characterization of network topology and internode dependencies for more informed defensive strategies.
  • To address the limitations of traditional methods in high-dimensional, sparse network environments.

Main Methods:

  • Constructed a game model for cyber confrontations to represent network topology elements for decision-making.
  • Combined the Node2vec graph embedding algorithm with reinforcement learning to enhance information characterization for defenders.
  • Utilized low-dimensional vector embeddings for node attributes and network structural features, moving beyond traditional one-hot encoding.
  • Extended the Cyberwheel algorithm training environment with new fine-grained defense mechanisms.

Main Results:

  • The proposed graph embedding-based decision-making method demonstrated superior performance compared to traditional perception methods in strategy selection for defensive decision-making.
  • Node2vec effectively embedded network attributes and structural features into low-dimensional vectors, overcoming perceptual limitations.
  • Analysis of Node2vec parameters confirmed their impact on embedding effectiveness for ACD decision-making.

Conclusions:

  • The integration of graph embedding with reinforcement learning offers a significant advancement in autonomous cyber defense decision-making.
  • The proposed method enhances topology awareness and enables dynamic strategy optimization for defenders.
  • This approach provides a more effective and robust solution for navigating complex and dynamic cyberspace environments.