# Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

arXiv: https://arxiv.org/abs/1702.05983 · HTML: https://mlsanity.com/paper/1702.05983/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2017.

## Built on by (most cited first, 30 of 75)

- [Towards quantum enhanced adversarial robustness in machine learning](https://mlsanity.com/paper/2306.12688/) (2023)
- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [EvilModel 2.0: Bringing Neural Network Models into Malware Attacks](https://mlsanity.com/paper/2109.04344/) (2021)
- [EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection](https://mlsanity.com/paper/2110.03301/) (2021)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector](https://mlsanity.com/paper/2301.01044/) (2023)
- [A Survey on Malware Detection with Graph Representation Learning](https://mlsanity.com/paper/2303.16004/) (2023)
- [Morphence: Moving Target Defense Against Adversarial Examples](https://mlsanity.com/paper/2108.13952/) (2021)
- [Defending Against Backdoor Attack on Graph Nerual Network by Explainability](https://mlsanity.com/paper/2209.02902/) (2022)
- [A Survey of Malware Detection Using Deep Learning](https://mlsanity.com/paper/2407.19153/) (2024)
- [Impacts and Risk of Generative AI Technology on Cyber Defense](https://mlsanity.com/paper/2306.13033/) (2023)
- [Explanation-Guided Diagnosis of Machine Learning Evasion Attacks](https://mlsanity.com/paper/2106.15820/) (2021)
- [Realizable Universal Adversarial Perturbations for Malware](https://mlsanity.com/paper/2102.06747/) (2021)
- [All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines](https://mlsanity.com/paper/2112.09219/) (2021)
- [A Comparison of Adversarial Learning Techniques for Malware Detection](https://mlsanity.com/paper/2308.09958/) (2023)
- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- [Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks](https://mlsanity.com/paper/2107.14776/) (2021)
- [Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review](https://mlsanity.com/paper/2206.02733/) (2022)
- [Generative Adversarial Networks and Image-Based Malware Classification](https://mlsanity.com/paper/2207.00421/) (2022)
- [Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets](https://mlsanity.com/paper/2308.10120/) (2023)
- [A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing](https://mlsanity.com/paper/2402.15267/) (2024)
- [Auxiliary-Classifier GAN for Malware Analysis](https://mlsanity.com/paper/2107.01620/) (2021)
- [Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights](https://mlsanity.com/paper/2108.12473/) (2021)
- [StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2202.07568/) (2022)
- [Creating Valid Adversarial Examples of Malware](https://mlsanity.com/paper/2306.13587/) (2023)
- [URET: Universal Robustness Evaluation Toolkit (for Evasion)](https://mlsanity.com/paper/2308.01840/) (2023)

## 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.