AdvAndMal: Adversarial Training for Android Malware Detection and Family Classification
| Autor: | Xusheng Wang, Kai Zhao, Xuhui Ding, Linlin Zhang, Chenyue Wang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Android malware
detection and classification adversarial training system call sequence visualization robustness coefficient Physics and Astronomy (miscellaneous) Computer science General Mathematics 0211 other engineering and technologies Sample (statistics) 02 engineering and technology Interval (mathematics) Machine learning computer.software_genre Adversarial system Robustness (computer science) QA1-939 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) 021110 strategic defence & security studies Sequence business.industry Training effect Visualization Chemistry (miscellaneous) Malware 020201 artificial intelligence & image processing Artificial intelligence business computer Mathematics |
| Zdroj: | Symmetry, Vol 13, Iss 1081, p 1081 (2021) Symmetry; Volume 13; Issue 6; Pages: 1081 |
| ISSN: | 2073-8994 |
| Popis: | In recent years, Android malware has continued to evolve against detection technologies, becoming more concealed and harmful, making it difficult for existing models to resist adversarial sample attacks. At the current stage, the detection result is no longer the only criterion for evaluating the pros and cons of the model with its algorithms, it is also vital to take the model’s defensive ability against adversarial samples into consideration. In this study, we propose a general framework named AdvAndMal, which consists of a two-layer network for adversarial training to generate adversarial samples and improve the effectiveness of the classifiers in Android malware detection and family classification. The adversarial sample generation layer is composed of a conditional generative adversarial network called pix2pix, which can generate malware variants to extend the classifiers’ training set, and the malware classification layer is trained by RGB image visualized from the sequence of system calls. To evaluate the adversarial training effect of the framework, we propose the robustness coefficient, a symmetric interval i = [−1, 1], and conduct controlled experiments on the dataset to measure the robustness of the overall framework for the adversarial training. Experimental results on 12 families with the largest number of samples in the Drebin dataset show that the accuracy of the overall framework is increased from 0.976 to 0.989, and its robustness coefficient is increased from 0.857 to 0.917, which proves the effectiveness of the adversarial training method. |
| Databáze: | OpenAIRE |
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