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

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

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While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features.Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level.Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a VITAMIN E 400IU novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.

Firstly, to ensure semantic richness, a Bidirectional Encoder Representations from Transformers-based (BERT) attribute-enhanced function embedding method is proposed for the extraction of node attributes in the function call graph.Subsequently, this work designs a hierarchical graph neural network that integrates attention mechanisms and pooling operations, complemented by function node sampling and structural learning strategies.This framework delivers a comprehensive profile of malicious code across semantic, syntactic, and structural dimensions.

Extensive experiments conducted on the Recovery - Training Equipment Kaggle and VirusShare datasets have demonstrated that the proposed framework outperforms other graph neural network (GNN)-based malware detection methods.