LSGAN-AT: enhancing malware detector robustness against adversarial examples

LSGAN-AT: enhancing malware detector robustness against adversarial examples

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 metho...

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Detalles Bibliográficos
Autores principales: Jianhua Wang, Xiaolin Chang, Yixiang Wang, Ricardo J. Rodríguez, Jianan Zhang
Formato: article
Lenguaje:EN
Publicado: SpringerOpen 2021
Materias:
Adversarial malware example
Generative adversarial network
Machine learning
Malware detector
Transferability
Computer engineering. Computer hardware
TK7885-7895
Electronic computers. Computer science
QA75.5-76.95
Acceso en línea:https://doaj.org/article/6cd390847dbd4392914b322c5efd1529
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