DexRay：一种基于字节码图像表示的简单而有效的安卓恶意软件检测深度学习方法
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
AI总结：
本文提出DexRay方法，将安卓DEX字节码转为灰度向量图像并用一维CNN检测恶意软件，在超15.8万应用上F1达0.96，为图像式恶意软件检测提供了简单有效且可扩展的基线。
摘要：
近年来，计算机视觉领域取得了多项进展，深度表示学习研究带来了前所未有的性能表现。因此，图像格式对其他领域（如恶意软件检测）也显得具有吸引力，在恶意软件检测中，基于图像的深度学习减少了对能够泛化到不同恶意软件变体的全面手工构造特征的需求。我们推测，这一研究方向可能成为安卓恶意软件检测的下一个前沿，因此需要一条清晰的路线图，以确保新方法确实带来新颖的贡献。我们通过开发并评估一个步骤直接的基于图像的恶意软件检测基线流程，做出了第一块构建模块的贡献。我们提出了DexRay，它将应用DEX文件的字节码转换为灰度“向量”图像，并将其输入一维卷积神经网络模型。我们认为DexRay具有基础性意义，因为其设计选择极其基础，从而可以推断出在恶意软件检测中使用基于图像的学习所能获得的最低性能。DexRay在超过158k个应用上的评估性能表明，尽管方法简单，但我们的方法是有效的，具有很高的检测率（F1分数=0.96）。最后，我们研究了时间衰减和图像缩放对DexRay性能的影响，并评估了其对混淆处理的鲁棒性。这篇进行中的工作论文通过提供一种合理、简单而有效的方法（并提供可用工件），为基于深度学习的恶意软件检测领域做出了贡献，该方法可以作为界定许多深层问题的基础，而这些问题是充分发展该领域所需研究的。
英文摘要：
Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such as malware detection, where deep learning on images alleviates the need for comprehensively hand-crafted features generalising to different malware variants. We postulate that this research direction could become the next frontier in Android malware detection, and therefore requires a clear roadmap to ensure that new approaches indeed bring novel contributions. We contribute with a first building block by developing and assessing a baseline pipeline for image-based malware detection with straightforward steps. We propose DexRay, which converts the bytecode of the app DEX files into grey-scale "vector" images and feeds them to a 1-dimensional Convolutional Neural Network model. We view DexRay as foundational due to the exceedingly basic nature of the design choices, allowing to infer what could be a minimal performance that can be obtained with image-based learning in malware detection. The performance of DexRay evaluated on over 158k apps demonstrates that, while simple, our approach is effective with a high detection rate (F1-score= 0.96). Finally, we investigate the impact of time decay and image-resizing on the performance of DexRay and assess its resilience to obfuscation. This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain.
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