most citedTowards interpreting ML-based automated malware detection models: a survey

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cs.LG2021

DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks

Yixiang Wang, Jiqiang Liu, Xiaolin Chang +2

White-box Adversarial Example (AE) attacks towards Deep Neural Networks (DNNs) have a more powerful destructive capacity than black-box AE attacks in the fields of AE strategies. H…

cs.LG2021★ 4 cited

Towards Interpretable Ensemble Learning for Image-based Malware Detection

Yuzhou Lin, Xiaolin Chang

Deep learning (DL) models for image-based malware detection have exhibited their capability in producing high prediction accuracy. But model interpretability is posing challenges t…