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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LSGAN-AT: enhancing malware detector robustness against adversarial examples

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