A Robust CNN for Malware Classification against Executable Adversarial Attack

A Robust CNN for Malware Classification against Executable Adversarial Attack

Deep-learning-based malware-detection models are threatened by adversarial attacks. This paper designs a robust and secure convolutional neural network (CNN) for malware classification. First, three CNNs with different pooling layers, including global average pooling (GAP), global max pooling (GMP),...

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Bibliographic Details
Main Authors: Yunchun Zhang, Jiaqi Jiang, Chao Yi, Hai Li, Shaohui Min, Ruifeng Zuo, Zhenzhou An, Yongtao Yu
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Electronics
Subjects:
malware detection
adversarial machine learning
malware images
Windows malware
Online Access:https://www.mdpi.com/2079-9292/13/5/989

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https://www.mdpi.com/2079-9292/13/5/989

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