Enhanced DNNs for malware classification with GAN-based adversarial training

Deep learning based malware classification gains momentum recently. However, deep learning models are vulnerable to adversarial perturbation attacks especially when applied in network security application. Deep neural network (DNN)-based malware classifiers by eating the whole bit sequences are also...

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Veröffentlicht in:	Journal of Computer Virology and Hacking Techniques 2021-06, Vol.17 (2), p.153-163
Hauptverfasser:	Zhang, Yunchun, Li, Haorui, Zheng, Yang, Yao, Shaowen, Jiang, Jiaqi
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Classification Classifiers Computer Science Deep learning Machine learning Malware Original Paper Performance degradation Perturbation Training
Online-Zugang:	Volltext
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