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Toward robust malware detection, we explore the attack surface of existingmalware detection systems. We conduct root-cause analyses of the practicalbinary-level black-box adversarial malware examples. Additionally, we uncoverthe sensitivity of volatile features within the detection engines and exhibittheir exploitability. Highlighting volatile information channels within thesoftware, we introduce three software pre-processing steps to eliminate theattack surface, namely, padding removal, software stripping, and inter-sectioninformation resetting. Further, to counter the emerging section injectionattacks, we propose a graph-based section-dependent information extractionscheme for software representation. The proposed scheme leverages aggregatedinformation within various sections in the software to enable robust malwaredetection and mitigate adversarial settings. Our experimental results show thattraditional malware detection models are ineffective against adversarialthreats. However, the attack surface can be largely reduced by eliminating thevolatile information. Therefore, we propose simple-yet-effective methods tomitigate the impacts of binary manipulation attacks. Overall, our graph-basedmalware detection scheme can accurately detect malware with an area under thecurve score of 88.32\% and a score of 88.19% under a combination of binarymanipulation attacks, exhibiting the efficiency of our proposed scheme.

Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations