19 citations · 22 across the 5 of their papers we have counts for
5 papers
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
Brian Etter, James Lee Hu, Mohammedreza Ebrahimi +3
Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the d…
Large Language Models for Conducting Advanced Text Analytics Information Systems Research
Benjamin M. Ampel, Chi-Heng Yang, James Hu +1
The exponential growth of digital content has generated massive textual datasets, necessitating the use of advanced analytical approaches. Large Language Models (LLMs) have emerged…
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…
Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach
James Lee Hu, Mohammadreza Ebrahimi, Hsinchun Chen
Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware va…
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)…