Adversarial Robustness of Deep Learning-based Malware Detectors via (De)Randomized Smoothing

arXiv:2402.15267v1 Announce Type: new
Abstract: Deep learning-based malware detectors have been shown to be susceptible to adversarial malware examples, i.e. malware examples that have been deliberately manipulated in order to avoid detection. In light of the vulnerability of deep learning detectors to subtle input file modifications, we propose a practical defense against adversarial malware examples inspired by (de)randomized smoothing. In this work, we reduce the chances of sampling adversarial content injected by malware authors by selecting correlated subsets of bytes, …

adversarial arxiv cs.ai cs.cr deep learning detection examples file input malware modifications order robustness vulnerability