most citedBinary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model

19 citations · 19 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CR2022

Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

James Lee Hu, Mohammadreza Ebrahimi, Weifeng Li +2

Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior an…

cs.CR2020★ 19 cited

Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model

Mohammadreza Ebrahimi, Ning Zhang, James Hu +2

Anti-malware engines are the first line of defense against malicious software. While widely used, feature engineering-based anti-malware engines are vulnerable to unseen (zero-day)…