23 citations · 89 across the 24 of their papers we have counts for
10 papers · 1 filter
Color Teams for Machine Learning Development
Josh Kalin, David Noever, Matthew Ciolino
Machine learning and software development share processes and methodologies for reliably delivering products to customers. This work proposes the use of a new teaming construct for…
A Survey of Machine Learning Algorithms for Detecting Ransomware Encryption Activity
Erik Larsen, David Noever, Korey MacVittie
A survey of machine learning techniques trained to detect ransomware is presented. This work builds upon the efforts of Taylor et al. in using sensor-based methods that utilize dat…
Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach
David Noever, Ryerson Burdick
The application of Generative Pre-trained Transformer (GPT-2) to learn text-archived game notation provides a model environment for exploring sparse reward gameplay. The transforme…
Reading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons
David A. Noever, Samantha E. Miller Noever
With Open AI's publishing of their CLIP model (Contrastive Language-Image Pre-training), multi-modal neural networks now provide accessible models that combine reading with visual…
A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models
Josh Kalin, David Noever, Matthew Ciolino
Machine learning models present a risk of adversarial attack when deployed in production. Quantifying the contributing factors and uncertainties using empirical measures could assi…
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks
Matthew Ciolino, Josh Kalin, David Noever
Production machine learning systems are consistently under attack by adversarial actors. Various deep learning models must be capable of accurately detecting fake or adversarial in…