39 citations · 49 across the 3 of their papers we have counts for
4 papers
Generating Practical Adversarial Network Traffic Flows Using NIDSGAN
Bolor-Erdene Zolbayar, Ryan Sheatsley, Patrick McDaniel +4
Network intrusion detection systems (NIDS) are an essential defense for computer networks and the hosts within them. Machine learning (ML) nowadays predominantly serves as the basi…
On the Robustness of Domain Constraints
Ryan Sheatsley, Blaine Hoak, Eric Pauley +3
Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs…
Adversarial Examples in Constrained Domains
Ryan Sheatsley, Nicolas Papernot, Michael Weisman +2
Machine learning algorithms have been shown to be vulnerable to adversarial manipulation through systematic modification of inputs (e.g., adversarial examples) in domains such as i…
Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments
Edwin Dauber, Aylin Caliskan, Richard Harang +4
Program authorship attribution has implications for the privacy of programmers who wish to contribute code anonymously. While previous work has shown that complete files that are i…