mlsanityData to 14 Oct 2026

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

arXiv 2309.01866 ↗First posted 5 Sep 2023cs.CR cs.AI cs.LGcited by 10

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Abstract (arXiv)

The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers. Machine learning-based (ML-based) Android malware detection (AMD) methods are crucial in addressing this problem; however, their vulnerability to adversarial examples raises concerns. Current attacks against ML-based AMD methods demonstrate remarkable performance but rely on strong assumptions that may not be realistic in real-world scenarios, e.g., the knowledge requirements about feature space, model parameters, and training dataset. To address this limitation, we introduce AdvDroidZero, an efficient query-based attack framework against ML-based AMD methods that operates under the zero knowledge setting. Our extensive evaluation shows that AdvDroidZero is effective against various mainstream ML-based AMD methods, in particular, state-of-the-art such methods and real-world antivirus solutions.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 35 of its 74 references that are in the map. First posted on arXiv 5 Sep 2023.

Builds on

35 of 74 references.

References · 35

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23 more
  1. 2018Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attackscited by 81
  2. 2018Interpretable Deep Learning under Firecited by 74
  3. 2016A Boundary Tilting Persepective on the Phenomenon of Adversarial Examplescited by 70
  4. 2022"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practicecited by 56
  5. 2020Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Driftcited by 53
  6. 2017Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detectioncited by 52
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  12. 2018Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detectioncited by 19
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  16. 2021EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detectioncited by 13
  17. 2023Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Informationcited by 13
  18. 2020Improving Certified Robustness via Statistical Learning with Logical Reasoningcited by 12
  19. 2021RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploitcited by 11
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 3.

10 papers.

  1. 2025Automatic Red Teaming LLM-based Agents with Model Context Protocol Toolscited by 18
  2. 2024SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systemscited by 7
  3. 2024Unraveling the Key of Machine Learning-based Android Malware Detectioncited by 4
  4. 2026FraudShield: Knowledge Graph Empowered Defense for LLMs against Fraud Attackscited by 4
  5. 2023MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attackscited by 3
  6. 2025Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systemscited by 2
  7. 2024MASKDROID: Robust Android Malware Detection with Masked Graph Representationscited by 1
  8. 2025On the Security Risks of ML-based Malware Detection Systems: A Surveycited by 1
  9. 2026Hybrid Analysis for Secure MCP Tool Use in LLM Agentscited by 0
  10. 2026REPLICANT: Learning Policies for Evading and Hardening Malware Detectorscited by 0