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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Bibliographic Details
Main Authors: Jianhua Wang, Xiaolin Chang, Yixiang Wang, Ricardo J. Rodríguez, Jianan Zhang
Format: Article
Language:English
Published: SpringerOpen 2021-12-01
Series:Cybersecurity
Subjects:
Adversarial malware example
Generative adversarial network
Machine learning
Malware detector
Transferability
Online Access:https://doi.org/10.1186/s42400-021-00102-9

Internet

https://doi.org/10.1186/s42400-021-00102-9

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