366 citations · 443 across the 4 of their papers we have counts for
4 papers
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
Keane Lucas, Mahmood Sharif, Lujo Bauer +2
Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes…
Deep Detector Health Management under Adversarial Campaigns
Javier Echauz, Keith Kenemer, Sarfaraz Hussein +4
Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control system…
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning
Jasjeet Dhaliwal, Saurabh Shintre
Deep neural networks are susceptible to small-but-specific adversarial perturbations capable of deceiving the network. This vulnerability can lead to potentially harmful consequenc…
Detecting Adversarial Samples from Artifacts
Reuben Feinman, Ryan R. Curtin, Saurabh Shintre +1
Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input. However, these models are vulnerable to adversari…