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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs.

Haojie Wu¹, Nurbol Luktarhan², Gaoqi Tian¹

¹School of Software, Xinjiang University, Urumqi 830091, China.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

This summary is machine-generated.

This study introduces an improved method for Android malware detection using function call graphs (FCGs) and graph neural networks (GNNs). The approach enhances node feature differences, leading to higher detection accuracy for malicious applications.

Failed At:

2026-06-19T13:40:13.505528+00:00

Keywords:

Android malware detection TF–IDF function call graph graph convolutional network self-loop

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