aiqbench analysis
MalTree introduces a scalable framework that applies bioinformatics-inspired phylogenetic techniques to automatically model malware evolution, achieving 87% temporal consistency with real-world emergence timelines. By shifting from reactive sample-by-sample classification to lineage-aware evolutionary modeling, the system enables detection strategies tailored to the specific mutation rates of different threat families. This approach offers a proactive defense mechanism that significantly reduces the time required to uncover complex lineage relationships compared to traditional reverse engineering.
MalTree: Tracing Malware Evolution from Embeddings at Scale
arXiv:2606.06570v1 Announce Type: cross Abstract: Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but traditional reverse engineering can take months to years to uncover such lineage relationships. We propose MalTree, a framework that applies bioinformatics inspired phylogenetic techniques (UPGMA and Neighbor-Joining) at scale to model malware evolution automatically using structural, behavioral, and image-based features. We introduce temporal validation using VirusTotal timestamps to assess whether inferred trees reflect actual evolutionary order. MalTree achieves 87% temporal consistency, indicating that inferred evolutionary relationships closely align with real-world emergence timelines. Our analysis shows that some families mutate over 10 times faster than others, suggesting that detection strategies should be tailored to family-specific evolutionary tempos. Case studies, including the Mirai botnet, confirm that inferred relationships from our phylogenetic tree align with documented threat intelligence. Our framework provides a foundation for shifting malware analysis from sample-by-sample classification toward lineage-aware evolutionary modeling.
Abstract:Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but traditional reverse engineering can take months to years to uncover such lineage relationships. We propose MalTree, a framework that applies bioinformatics inspired phylogenetic techniques (UPGMA and Neighbor-Joining) at scale to model malware evolution automatically using structural, behavioral, and image-based features. We introduce temporal validation using VirusTotal timestamps to assess whether inferred trees reflect actual evolutionary order. MalTree achieves 87% temporal consistency, indicating that inferred evolutionary relationships closely align with real-world emergence timelines. Our analysis shows that some families mutate over 10 times faster than others, suggesting that detection strategies should be tailored to family-specific evolutionary tempos. Case studies, including the Mirai botnet, confirm that inferred relationships from our phylogenetic tree align with documented threat intelligence. Our framework provides a foundation for shifting malware analysis from sample-by-sample classification toward lineage-aware evolutionary modeling.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2606.06570 [cs.CR]
(or arXiv:2606.06570v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.06570 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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