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This is a Quantitative Study study.

October 16, 2025Open Access

Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis

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Authors

ZZZhonghao ZhanImperial College London HZHuichi ZhouQilu University of Technology HHHamed HaddadiImperial College London

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Implication

This research demonstrates improved robustness against adversarial attacks in GNN-based network intrusion detection, showing the value of agent-based analysis with large language models.

Key Points

Integrating LLMs as agents improves GNN resilience against adversarial attacks in network intrusion detection.
Experiments show that agent-based analysis significantly boosts GNN performance in realistic evaluation scenarios.
Adversarial attacks tested include various black-box and white-box scenarios, highlighting the robustness of the approach.
Utilizing network flow data, agent-based systems can identify and mitigate threats before GNN processing.

Cite This Study

Zhan et al. (2025) studied this question.

synapsesocial.com/papers/68f04935e559138a1a06e59e https://doi.org/10.48550/arxiv.2506.20806

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Implication

This research demonstrates improved robustness against adversarial attacks in GNN-based network intrusion detection, showing the value of agent-based analysis with large language models.

Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

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