Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the sensitivity of volatile features within the detection engines and exhibit their exploitability. Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting. Further, to counter the emerging section injection attacks, we propose a graph-based section-dependent information extraction scheme for software representation. The proposed scheme leverages aggregated information within various sections in the software to enable robust malware detection and mitigate adversarial settings. Our experimental results show that traditional malware detection models are ineffective against adversarial threats. However, the attack surface can be largely reduced by eliminating the volatile information. Therefore, we propose simple-yet-effective methods to mitigate the impacts of binary manipulation attacks. Overall, our graph-based malware detection scheme can accurately detect malware with an area under the curve score of 88.32\% and a score of 88.19% under a combination of binary manipulation attacks, exhibiting the efficiency of our proposed scheme.

翻译：为实现鲁棒的恶意软件检测，我们探索了现有恶意软件检测系统的攻击面。我们对实际的二进制层面黑盒对抗性恶意软件样本进行了根因分析。此外，我们揭示了检测引擎中易变特征的敏感性，并展示了其可被利用的特性。通过强调软件中易变的信息通道，我们引入了三项软件预处理步骤以消除攻击面，即填充移除、软件剥离和节间信息重置。进一步，为应对新兴的节注入攻击，我们提出了一种基于图的节依赖信息提取方案用于软件表征。该方案利用软件中各节内的聚合信息，以实现鲁棒的恶意软件检测并缓解对抗性场景。实验结果表明，传统恶意软件检测模型在对抗性威胁面前无效。然而，通过消除易变信息，攻击面可大幅缩减。因此，我们提出了简单而有效的方法来减轻二进制操作攻击的影响。总体而言，我们基于图的恶意软件检测方案能够准确检测恶意软件，在多种二进制操作攻击组合下曲线下面积得分为88.32%，准确率为88.19%，展现了所提方案的有效性。