Adversarial Attacks on Transformers-Based Malware Detectors. (arXiv:2210.00008v1 [cs.CR])

Signature-based malware detectors have proven to be insufficient as even a
small change in malignant executable code can bypass these signature-based
detectors. Many machine learning-based models have been proposed to efficiently
detect a wide variety of malware. Many of these models are found to be
susceptible to adversarial attacks - attacks that work by generating
intentionally designed inputs that can force these models to misclassify. Our
work aims to explore vulnerabilities in the current state of the art malware
detectors …

adversarial attacks malware transformers