Software debloating techniques are applied to craft a specialized version of the program based on the user's requirements and remove irrelevant code accordingly. The debloated programs presumably maintain better performance and reduce the attack surface in contrast to the original programs. This work unleashes the effectiveness of applying software debloating techniques on the robustness of machine learning systems in the malware classification domain. We empirically study how an adversarial can leverage software debloating techniques to mislead machine learning malware classification models. We apply software debloating techniques to generate adversarial examples and demonstrate these adversarial examples can reduce the detection rate of VirusTotal. Our study opens new directions for research into adversarial machine learning not only in malware detection/classification but also in other software domains.

翻译：软件精简技术被应用于根据用户需求定制程序的专用版本，并相应移除无关代码。精简化程序相较于原始程序理论上能保持更优性能并缩减攻击面。本研究揭示了在恶意软件分类领域应用软件精简技术对机器学习系统鲁棒性的实际影响。我们通过实证研究探讨攻击者如何利用软件精简技术误导机器学习恶意软件分类模型。通过应用软件精简技术生成对抗样本，我们证明这些对抗样本能够降低VirusTotal的检测率。这项研究不仅为恶意软件检测/分类领域的对抗性机器学习开辟了新方向，也为其他软件领域的相关研究提供了新思路。