55 citations · 72 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
Rouzbeh Behnia, Arman Riasi, Reza Ebrahimi +3
Secure aggregation protocols ensure the privacy of users' data in federated learning by preventing the disclosure of local gradients. Many existing protocols impose significant com…
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…
Privately Fine-Tuning Large Language Models with Differential Privacy
Rouzbeh Behnia, Mohamamdreza Ebrahimi, Jason Pacheco +1
Pre-trained Large Language Models (LLMs) are an integral part of modern AI that have led to breakthrough performances in complex AI tasks. Major AI companies with expensive infrast…
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…