Journal
RESEARCH IN ATTACKS, INTRUSIONS, AND DEFENSES, RAID 2018
Volume 11050, Issue -, Pages 490-510Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-00470-5_23
Keywords
Adversarial attacks; Malware classification; Deep neural networks; Dynamic analysis; Transferability
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In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM. We also implement GADGET, a software framework to convert any malware binary to a binary undetected by malware classifiers, using the proposed attack, without access to the malware source code.
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