mlsanityData to 14 Oct 2026

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

arXiv 2310.03285 ↗First posted 5 Oct 2023cs.LG cs.CRcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 47 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

19 of 47 references.

References · 19

  1. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  2. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  3. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 546
  4. 2017Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networkscited by 486
  5. 2017On Detecting Adversarial Perturbationscited by 232
  6. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
  7. 2017Malware Detection by Eating a Whole EXEcited by 115
  8. 2016Adversarial Examples Detection in Deep Networks with Convolutional Filter Statisticscited by 77
  9. 2018Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executablescited by 62
  10. 2015Distributional Smoothing with Virtual Adversarial Trainingcited by 61
  11. 2020Functionality-preserving Black-box Optimization of Adversarial Windows Malwarecited by 58
  12. 2018Exploring Adversarial Examples in Malware Detectioncited by 42
7 more
  1. 2018Adversarial Deep Learning for Robust Detection of Binary Encoded Malwarecited by 32
  2. 2019Dynamic Malware Analysis with Feature Engineering and Feature Learningcited by 23
  3. 2018Lightweight Classification of IoT Malware based on Image Recognitioncited by 13
  4. 2016Adversary Resistant Deep Neural Networks with an Application to Malware Detectioncited by 9
  5. 2017Learning the PE Header, Malware Detection with Minimal Domain Knowledgecited by 7
  6. 2019Detecting and Classifying Android Malware using Static Analysis along with Creator Informationcited by 7
  7. 2018Malytics: A Malware Detection Schemecited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineeringcited by 15
  2. 2025Empirical Evaluation of Concept Drift in ML-Based Android Malware Detectioncited by 2
  3. 2026Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detectioncited by 0