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InfoResearchPeer-reviewed
Towards few-shot malware classification with fine-grained and pattern-aware multi-prototype network
- Published
- Record updated
Summary
Researchers propose FIPAPNet, a few-shot learning network for classifying malware from dynamic analysis. It converts system call sequences into feature images, combining Autoencoder pre-training, a Siamese Neural Network, a Multi-prototype Network and DBSCAN clustering. The authors report 93% accuracy on 5-way 5-shot tasks, which they say surpasses existing few-shot dynamic analysis methods.