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Multimodal Deep Learning for Android Malware Classification
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DOI：10.3390/make7010023.png)
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Abstract
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This study investigates the integration of diverse data modalities within deep learning ensembles for Android malware classification. Android applications can be represented as binary images and function call graphs, each offering complementary perspectives on the executable. We synthesise these modalities by combining predictions from convolutional and graph neural networks with a multilayer perceptron. Empirical results demonstrate that multimodal models outperform their unimodal counterparts while remaining highly efficient. For instance, integrating a plain CNN with 83.1% accuracy and a GCN with 80.6% accuracy boosts overall accuracy to 88.3%. DenseNet-GIN achieves 90.6% accuracy, with no further improvement obtained by expanding this ensemble to four models. Based on our findings, we advocate for the flexible development of modalities to capture distinct aspects of applications and for the design of algorithms that effectively integrate this information.
Keywords:
multimodal deep learning for Android malware detection
enhanced malware analysis
graph neural networks
function call graphs (FCG)
efficient multimodal late fusion
CNN GNN Ensemble
bytecode image analysis
Android APK analysis
data fusion
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