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MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural

Wenjie Guo¹, Wenbiao Du¹, Xiuqi Yang¹

¹School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

This study introduces MalHAPGNN, a novel deep learning framework for malware detection. It enhances feature embedding using enhanced call graphs and BERT, outperforming existing graph neural network methods.

Keywords:

graph neural network graph pooling mechanism malware detection malware embedding representation learning

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Deep learning in malware detection often misses semantic and structural features.
Existing methods focus on superficial characteristics, lacking in-depth analysis.

Purpose of the Study:

To introduce MalHAPGNN, a novel framework for enhanced malware detection.
To address limitations in current neural network-based malware feature embedding.

Main Methods:

Utilized a hierarchical attention pooling graph neural network (GNN) with enhanced call graphs.
Employed Bidirectional Encoder Representations from Transformers (BERT) for attribute-enhanced function embedding.
Integrated function node sampling and structural learning strategies.

Main Results:

MalHAPGNN provides a comprehensive profile of malicious code across semantic, syntactic, and structural dimensions.
Experiments on Kaggle and VirusShare datasets show superior performance compared to other GNN-based methods.

Conclusions:

MalHAPGNN offers a significant advancement in malware detection capabilities.
The framework effectively captures deep semantic and structural information for improved accuracy.