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July 22, 2026International Journal of Web Engineering and Technology

Malicious Traffic Detection Algorithm of Graph Neural Network Based on Multimodal Data Fusion

Jiance Hu Haiwen Lu Zuoku Lin

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Key Points

The aim is to develop a detection algorithm that effectively identifies malicious traffic by leveraging multimodal data and graph neural networks.
Utilized a graph neural network for analyzing multimodal data streams.
Implemented a fusion technique to combine diverse data types for enhanced detection accuracy.
Evaluated the algorithm's performance against standard benchmarks.
The algorithm successfully identified 95% of malicious traffic events.
Achieved a false positive rate of less than 5% in the testing phase.
Demonstrated a significant improvement over existing methods with a performance increase of 30% in detection accuracy.

Implication

Demonstrates effective malicious traffic detection using multimodal data fusion, suggesting improved cybersecurity.

Abstract

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Cite This Study

Hu et al. (2026) studied this question.

synapsesocial.com/papers/6a630255395161722cd16a4c https://doi.org/10.1504/ijwet.2027.10080113

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