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InfoResearchPeer-reviewed
DynMD: Energy-Based Dynamic Graph Representation Learning for Malware Detection
- Published
- Record updated
Summary
DynMD is a GNN model for malware detection on streaming behavioral data. It uses an energy function to adaptively partition windows into a lossless malware dynamic heterogeneous graph, with intra-graph and inter-graph message propagation. On three real-world malware datasets it improves detection accuracy by up to 3.99% and runs 3.81× to 5.33× faster than MG-DVD, identifying malware within an average of 40 seconds of execution.