Adversarial Attacks on Transformers-Based Malware Detectors
This addresses security risks in AI-driven malware detection systems, though it appears incremental as it applies known adversarial attack methods to a specific domain.
The paper investigates vulnerabilities in state-of-the-art Transformers-based malware detectors to adversarial attacks, achieving a 23.9% misclassification rate with attacks and reducing it by half with proposed defenses.
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 to adversarial attacks. We train a Transformers-based malware detector, carry out adversarial attacks resulting in a misclassification rate of 23.9% and propose defenses that reduce this misclassification rate to half. An implementation of our work can be found at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.