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English(EN) REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

Replicant框架学会规避和加固恶意软件检测器

作者 PulseAugur 编辑部 · [1 个来源] ·

一个名为Replicant的新框架已被开发出来，用于学习如何规避和加固恶意软件检测器。这种深度强化学习方法在严格的黑盒威胁模型下运行，这意味着它不需要访问检测器的内部工作原理。Replicant在七个Android恶意软件检测器上展示了78.8%的攻击成功率，优于现有的最先进方法。该框架学习到的策略可以在不同的样本、检测器和特征空间中重复使用，并且它在对抗性训练中也证明了有效性，可以创建更健壮的检测器。 AI

影响 这项研究通过理解和模拟复杂的对抗性攻击，可能导致更健壮的恶意软件检测系统。

排序理由 研究论文，详细介绍了用于恶意软件检测规避的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Replicant框架学会规避和加固恶意软件检测器

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研究论文，详细介绍了用于恶意软件检测规避的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia, Alexander Herzog, Myles Foley, Chris Hicks, Lorenzo Cavallaro, Fabio Pierazzi ·

    REPLICANT：学习用于规避和加固恶意软件检测器的策略

    arXiv:2608.28499v1 Announce Type: new Abstract: To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-the-art attacks do not effectively model realistic a…

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