mlsanityData to 14 Oct 2026

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

arXiv 2312.06423 ↗First posted 11 Dec 2023cs.CR cs.AI cs.LGcited by 3

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Abstract (arXiv)

Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of ML-based detection systems to evasion attacks. While efforts have been made to address this critical issue, many of the existing defensive methods encounter challenges such as lower effectiveness or reduced generalization capabilities. In this paper, we introduce MalPurifier, a novel adversarial purification framework specifically engineered for Android malware detection. Specifically, MalPurifier integrates three key innovations: a diversified adversarial perturbation mechanism for robustness and generalizability, a protective noise injection strategy for benign data integrity, and a Denoising AutoEncoder (DAE) with a dual-objective loss for accurate purification and classification. Extensive experiments on two large-scale datasets demonstrate that MalPurifier significantly outperforms state-of-the-art defenses. It robustly defends against a comprehensive set of 37 perturbation-based evasion attacks, consistently achieving robust accuracies above 90.91%. As a lightweight, model-agnostic, and plug-and-play module, MalPurifier offers a practical and effective solution to bolster the security of ML-based Android malware detectors.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 68 references that are in the map. First posted on arXiv 11 Dec 2023.

Builds on

25 of 68 references.

References · 25

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,451
  3. 2022Diffusion Models for Adversarial Purificationcited by 476
  4. 2017On the (Statistical) Detection of Adversarial Examplescited by 180
  5. 2020A Self-supervised Approach for Adversarial Robustnesscited by 180
  6. 2021Adversarial purification with Score-based generative modelscited by 134
  7. 2018Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attackscited by 81
  8. 2017Generating Adversarial Malware Examples for Black-Box Attacks Based on GANcited by 75
  9. 2019Interpreting Adversarially Trained Convolutional Neural Networkscited by 75
  10. 2022Evaluating the Adversarial Robustness of Adaptive Test-time Defensescited by 64
  11. 2017Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detectioncited by 52
  12. 2021Boosting Fast Adversarial Training with Learnable Adversarial Initializationcited by 40
13 more
  1. 2021Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descentcited by 35
  2. 2020Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detectioncited by 33
  3. 2018Adversarial Deep Learning for Robust Detection of Binary Encoded Malwarecited by 32
  4. 2018Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detectioncited by 19
  5. 2021EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detectioncited by 13
  6. 2022Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Modelscited by 13
  7. 2023Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Informationcited by 13
  8. 2019DL-Droid: Deep learning based android malware detection using real devicescited by 11
  9. 2021Interpolated Joint Space Adversarial Training for Robust and Generalizable Defensescited by 10
  10. 2023Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Settingcited by 10
  11. 2021OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaScited by 7
  12. 2023PAD: Towards Principled Adversarial Malware Detection Against Evasion Attackscited by 5
  13. 2023Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustnesscited by 4

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systemscited by 2
  2. 2025On the Security Risks of ML-based Malware Detection Systems: A Surveycited by 1
  3. 2025Evaluating the robustness of adversarial defenses in malware detection systemscited by 0