Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers

Understand

In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers.

• We generate adversarial examples by modifying the malware's API call sequences and non-sequential features (printable strings), and these adversarial examples will be misclassified by the target malware classifier without affecting the malware's functionality.
• In contrast to previous studies, our attack minimizes the number of malware classifier queries required.
• In addition, in our attack, the attacker must only know the class predicted by the malware classifier; attacker knowledge of the malware classifier's confidence score is optional.