← Back to all news
arXiv Machine Learning By Ngoc N. Tran, Anwar Said, Waseem Abbas, Tyler Derr, Xenofon D. Koutsoukos

Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark

Read the original on arXiv Machine Learning →

The paper introduces a new benchmark for assessing out-of-distribution robustness in graph-based Android malware classifiers, highlighting that current models drop up to 45% accuracy on unseen malware variants. It presents two scenarios—MalNet-Tiny-Common for covariate shift and MalNet-Tiny-Distinct for domain shift—and identifies a limitation in existing benchmarks that rely solely on structure-only function call graphs. To address this, the authors propose a semantic enrichment framework that augments graph topology with function-level attributes and LLM-based code embeddings, demonstrating that this data-centric approach improves robustness under distribution shift and complements model-based methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 23

HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning

HYDRA is a proactive Android malware drift adaptation framework that learns drift‑invariant representations from hierarchically structured data. It combines fine‑grained Control Flow Graphs and coarse‑grained Function Call Graphs to model applications, then applies a cross‑domain contrastive learning objective to align historical and new data distributions. Experiments on large‑scale, time‑ordered malware datasets show HYDRA achieves lower false negative and false positive rates than state‑of‑the‑art baselines while needing up to 87.5% fewer labeled samples.

By Han Chen, Hanchen Wang, Hongmei Chen, Lu Qin, Wenjie Zhang, Ying Zhang
benchmarkssafety
More like this →