On Training Robust PDF Malware Classifiers

6 Apr 2019Yizheng ChenShiqi WangDongdong SheSuman Jana

Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks... (read more)

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