Biblio

As the worldwide internet has non-stop developments, it comes with enormous amount automatically generated malware. Those malware had become huge threaten to computer users. A comprehensive malware family classifier can help security researchers to quickly identify characteristics of malware which help malware analysts to investigate in more efficient way. However, despite the assistance of the artificial intelligent (AI) classifiers, it has been shown that the AI-based classifiers are vulnerable to so-called adversarial attacks. In this paper, we demonstrate how the adversarial settings can be applied to the classifier of malware families classification. Our experimental results achieved high successful rate through the adversarial attack. We also find the important features which are ignored by malware analysts but useful in the future analysis.