English(EN) Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection

研究人员提出用于恶意软件检测的可解释图神经网络集成模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员开发了一种新颖的集成框架，使用堆叠图神经网络（GNN）来改进恶意软件检测。该方法从可执行文件中动态提取控制流图，并使用多个GNN基础学习器来捕获多样化的行为特征。一个基于注意力的元学习器聚合预测，并通过量化每个基础模型的贡献来提供解释，从而增强了可解释性和鲁棒性。 AI

影响增强了人工智能驱动的恶意软件检测系统的可解释性和准确性。

排序理由这是一篇研究论文，详细介绍了使用GNN进行恶意软件检测的新颖技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins, Ali A Ghorbani · 2026-05-26 04:00

用于恶意软件检测的可解释注意力引导堆叠图神经网络

arXiv:2508.09801v3 Announce Type: replace-cross Abstract: Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks (GNNs) have shown promise in this domain by modeli…