mlsanityData to 7 Oct 2026

Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing

arXiv 2308.08906 ↗First posted 17 Aug 2023cs.CR cs.AIcited by 5

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Abstract (arXiv)

Malware detectors based on deep learning (DL) have been shown to be susceptible to malware examples that have been deliberately manipulated in order to evade detection, a.k.a. adversarial malware examples. More specifically, it has been show that deep learning detectors are vulnerable to small changes on the input file. Given this vulnerability of deep learning detectors, we propose a practical defense against adversarial malware examples inspired by randomized smoothing. In our work, instead of employing Gaussian or Laplace noise when randomizing inputs, we propose a randomized ablation-based smoothing scheme that ablates a percentage of the bytes within an executable. During training, our randomized ablation-based smoothing scheme trains a base classifier based on ablated versions of the executable files. At test time, the final classification for a given input executable is taken as the class most commonly predicted by the classifier on a set of ablated versions of the original executable. To demonstrate the suitability of our approach we have empirically evaluated the proposed ablation-based model against various state-of-the-art evasion attacks on the BODMAS dataset. Results show greater robustness and generalization capabilities to adversarial malware examples in comparison to a non-smoothed classifier.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 17 Aug 2023.

Builds on

18 of 27 references.

References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  5. 2017Evasion Attacks against Machine Learning at Test Timecited by 686
  6. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 462
  7. 2018Certified Adversarial Robustness with Additive Noisecited by 178
  8. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 164
  9. 2017Malware Detection by Eating a Whole EXEcited by 114
  10. 2018Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executablescited by 60
  11. 2020Functionality-preserving Black-box Optimization of Adversarial Windows Malwarecited by 58
  12. 2015Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Featurescited by 48
6 more
  1. 2018Exploring Adversarial Examples in Malware Detectioncited by 41
  2. 2018Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Datacited by 37
  3. 2019Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytescited by 31
  4. 2015Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classificationcited by 21
  5. 2019ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentationcited by 9
  6. 2022Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classificationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 1.

5 papers.

  1. 2024A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothingcited by 4
  2. 2024Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Researchcited by 1
  3. 2024PBP: Post-training Backdoor Purification for Malware Classifierscited by 1
  4. 2024Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothingcited by 0
  5. 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usabilitycited by 0