most citedHoneyModels: Machine Learning Honeypots

6 citations · 15 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CR2022

Adversarial Plannning

Valentin Vie, Ryan Sheatsley, Sophia Beyda +4

Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate t…

cs.LG20223 cited

A Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting

Kyle Domico, Ryan Sheatsley, Yohan Beugin +2

Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun, are an uncontrollable threat to modern technology. Notab…

cs.CR20226 cited

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…

cs.LG2022

Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

Ryan Sheatsley, Matthew Durbin, Azaree Lintereur +1

One of the principal uses of physical-space sensors in public safety applications is the detection of unsafe conditions (e.g., release of poisonous gases, weapons in airports, tain…

cs.CR20226 cited

HoneyModels: Machine Learning Honeypots

Ahmed Abdou, Ryan Sheatsley, Yohan Beugin +2

Machine Learning is becoming a pivotal aspect of many systems today, offering newfound performance on classification and prediction tasks, but this rapid integration also comes wit…

cs.CR2022

Building a Privacy-Preserving Smart Camera System

Yohan Beugin, Quinn Burke, Blaine Hoak +5

Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require user…