Lookin' Out My Backdoor! Investigating Backdooring Attacks Against DL-driven Malware Detectors
Mario D’Onghia, Federico Di Cesare, Luigi Gallo, Michele Carminati, Mario Polino, Stefano Zanero · 2023
Given their generalization capabilities,deep learning algorithms may represent a powerful weapon in the arsenal of antivirus developers. Nevertheless, recent works in different domains (e.g., computer vision) have shown that such algorithms are susceptible to backdooring attacks, namely training-time attacks that aim toteach a deep neural network to misclassify inputs containing a specific trigger. This work investigates the resilience of deep learning models for malware detection against backdooring attacks. In particular, we devise two classes of attacks for backdooring a malware detector that targets the update process of the underlying deep learning classifier. While the first and most straightforward approach relies onsuperficial triggers made of static byte sequences, the second attack we propose employslatent triggers, namely specific feature configurations in the latent space of the model. The latent triggers may be produced by different byte sequences in the binary inputs, rendering the triggerdynamic in the input space and thus more challenging to detect.