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...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Jianhua Wang, Xiaolin Chang, Yixiang Wang, Ricardo J. Rodríguez, Jianan Zhang
Format: article
Langue:EN
Publié: SpringerOpen 2021
Sujets:
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
Accès en ligne:https://doaj.org/article/6cd390847dbd4392914b322c5efd1529
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!

Internet

https://doaj.org/article/6cd390847dbd4392914b322c5efd1529

Documents similaires