Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
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Abstract (arXiv)
Machine learning has been used to detect new malware in recent years, while malware authors have strong motivation to attack such algorithms. Malware authors usually have no access to the detailed structures and parameters of the machine learning models used by malware detection systems, and therefore they can only perform black-box attacks. This paper proposes a generative adversarial network (GAN) based algorithm named MalGAN to generate adversarial malware examples, which are able to bypass black-box machine learning based detection models. MalGAN uses a substitute detector to fit the black-box malware detection system. A generative network is trained to minimize the generated adversarial examples' malicious probabilities predicted by the substitute detector. The superiority of MalGAN over traditional gradient based adversarial example generation algorithms is that MalGAN is able to decrease the detection rate to nearly zero and make the retraining based defensive method against adversarial examples hard to work.
Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2017.
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Citing papers per year: 2021: 22; 2022: 12; 2023: 14; 2024: 14; 2025: 5; 2026: 7.
74 papers.
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50 most cited of 74 All 74 (JSON)
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- 2024EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection27 Jul 2024
- 2024A Survey of Malware Detection Using Deep Learning27 Jul 2024
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- 2024How to Train your Antivirus: RL-based Hardening through the Problem-Space29 Feb 2024
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- 2024MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks17 Jan 2024
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- 2023When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence23 Nov 2023
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- 2023Towards quantum enhanced adversarial robustness in machine learning22 Jun 2023
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- 2022Generative Adversarial Networks and Image-Based Malware Classification8 Jun 2022
- 2022Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review6 Jun 2022
- 2022MERLIN -- Malware Evasion with Reinforcement LearnINg24 Mar 2022
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- 2021Deep Learning for Android Malware Defenses: a Systematic Literature Reviewcited by 18
- 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
- 2021Security and Privacy for Artificial Intelligence: Opportunities and Challengescited by 16
- 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
- 2021EvilModel 2.0: Bringing Neural Network Models into Malware Attackscited by 13
- 2021EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detectioncited by 13
- 2021Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluationcited by 11
- 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
- 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 10
- 2021Morphence: Moving Target Defense Against Adversarial Examplescited by 9
- 2022Defending Against Backdoor Attack on Graph Nerual Network by Explainabilitycited by 9
12 most cited of 74 All 74 (JSON)
- 2026Binary Iterative Method for Non-targeted Adversarial Attack5 Jul 2026
- 2026Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection22 May 2026
- 2026Learning to Look Benign: Targeted Evasion of Malware Detectors via API Import Injection18 May 2026
- 2026From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists6 May 2026
- 2026Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations23 Apr 2026
- 2026Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs4 Apr 2026
- 2026Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks20 Jan 2026
- 2025IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense23 Dec 2025
- 2025Synthetic Data: AI's New Weapon Against Android Malware24 Nov 2025
- 2025DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis30 Sep 2025
- 2025Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices13 Jun 2025
- 2025Evaluating the robustness of adversarial defenses in malware detection systems14 May 2025
12 newest of 74 All 74 (JSON)