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.
@article{arxiv.1707.05970,
title = {Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers},
author = {Ishai Rosenberg and Asaf Shabtai and Lior Rokach and Yuval Elovici},
journal= {arXiv preprint arXiv:1707.05970},
year = {2018}
}