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LSGAN-AT: enhancing malware detector robustness against adversarial examples
- Source :
- Cybersecurity, Vol 4, Iss 1, Pp 1-15 (2021)
- Publication Year :
- 2021
- Publisher :
- SpringerOpen, 2021.
-
Abstract
- Abstract Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN module and AT module. LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples. AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD). Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack. The results also verify the performance of the generated RMD in the recognition rate of AME.
Details
- Language :
- English
- ISSN :
- 25233246
- Volume :
- 4
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Cybersecurity
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.6cd390847dbd4392914b322c5efd1529
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s42400-021-00102-9