mlsanityData to 7 Oct 2026

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

arXiv 1704.08996 ↗First posted 28 Apr 2017cs.CRcited by 51

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Abstract (arXiv)

To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In other words, machine learning itself can be the weakest link in a security system. In this paper, we rely upon a previously-proposed attack framework to categorize potential attack scenarios against learning-based malware detection tools, by modeling attackers with different skills and capabilities. We then define and implement a set of corresponding evasion attacks to thoroughly assess the security of Drebin, an Android malware detector. The main contribution of this work is the proposal of a simple and scalable secure-learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of attack. We finally argue that our secure-learning approach can also be readily applied to other malware detection tasks.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Apr 2017.

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Citing papers per year: 2021: 12; 2022: 14; 2023: 11; 2024: 7; 2025: 6; 2026: 1.

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  1. 2021Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examplescited by 23
  2. 2022The Role of Machine Learning in Cybersecuritycited by 21
  3. 2023RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletioncited by 17
  4. 2022Poisoning Attacks and Defenses on Artificial Intelligence: A Surveycited by 15
  5. 2022Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifierscited by 14
  6. 2022Adversarial Robustness for Tabular Data through Cost and Utility Awarenesscited by 14
  7. 2021Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Differencecited by 13
  8. 2021EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detectioncited by 13
  9. 2023SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detectioncited by 13
  10. 2023Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Settingcited by 10
  11. 2023It Is All About Data: A Survey on the Effects of Data on Adversarial Robustnesscited by 9
  12. 2024TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)cited by 9
38 more
  1. 2023MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detectioncited by 8
  2. 2021Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectorscited by 7
  3. 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learningcited by 7
  4. 2021Explanation-Guided Diagnosis of Machine Learning Evasion Attackscited by 6
  5. 2022Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learningcited by 6
  6. 2021Realizable Universal Adversarial Perturbations for Malwarecited by 5
  7. 2021Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?cited by 5
  8. 2022When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classificationcited by 5
  9. 2022Mitigating Adversarial Gray-Box Attacks Against Phishing Detectorscited by 5
  10. 2023PAD: Towards Principled Adversarial Malware Detection Against Evasion Attackscited by 5
  11. 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challengescited by 5
  12. 2021Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weightscited by 3
  13. 2022StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detectioncited by 3
  14. 2023Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectorscited by 3
  15. 2023MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attackscited by 3
  16. 2021Learning Explainable Representations of Malware Behaviorcited by 2
  17. 2021Efficient Detection of Botnet Traffic by features selection and Decision Treescited by 2
  18. 2022Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malwarecited by 2
  19. 2025Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systemscited by 2
  20. 2022MaMaDroid2.0 -- The Holes of Control Flow Graphscited by 1
  21. 2022Open-Source Framework for Encrypted Internet and Malicious Traffic Classificationcited by 1
  22. 2023On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunitiescited by 1
  23. 2023ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learningcited by 1
  24. 2024ModSec-Learn: Boosting ModSecurity with Machine Learningcited by 1
  25. 2024On the Effectiveness of Adversarial Training on Malware Classifierscited by 1
  26. 2025On the Security Risks of ML-based Malware Detection Systems: A Surveycited by 1
  27. 2021Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchaincited by 0
  28. 2021"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detectorcited by 0
  29. 2022Adversarial Patterns: Building Robust Android Malware Classifierscited by 0
  30. 2022PhilaeX: Explaining the Failure and Success of AI Models in Malware Detectioncited by 0
  31. 2023Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detectioncited by 0
  32. 2024CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distributioncited by 0
  33. 2024Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlationscited by 0
  34. 2024A Novel Reinforcement Learning Model for Post-Incident Malware Investigationscited by 0
  35. 2025Enhancing Android Malware Detection with Retrieval-Augmented Generationcited by 0
  36. 2025Certifiably robust malware detectors by designcited by 0
  37. 2025DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detectioncited by 0
  38. 2025One Detector Fits All: Robust and Adaptive Detection of Malicious Packages from PyPI to Enterprisescited by 0

50 most cited of 51 All 51 (JSON)