110 citations · 280 across the 12 of their papers we have counts for
14 papers
Catastrophic Forgetting in the Context of Model Updates
Rich Harang, Hillary Sanders
A large obstacle to deploying deep learning models in practice is the process of updating models post-deployment (ideally, frequently). Deep neural networks can cost many thousands…
Magnificent Minified Models
Rich Harang, Hillary Sanders
This paper concerns itself with the task of taking a large trained neural network and 'compressing' it to be smaller by deleting parameters or entire neurons, with minimal decrease…
SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
Richard Harang, Ethan M. Rudd
In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million) dataset: a large-scale dataset consisting of nearly 20 million files with pre-extracted features and metad…
CATBERT: Context-Aware Tiny BERT for Detecting Social Engineering Emails
Younghoo Lee, Joshua Saxe, Richard Harang
Targeted phishing emails are on the rise and facilitate the theft of billions of dollars from organizations a year. While malicious signals from attached files or malicious URLs in…
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
Ethan M. Rudd, Felipe N. Ducau, Cody Wild +2
Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or…
Statistical Models for the Number of Successful Cyber Intrusions
Nandi O. Leslie, Richard E. Harang, Lawrence P. Knachel +1
We propose several generalized linear models (GLMs) to predict the number of successful cyber intrusions (or "intrusions") into an organization's computer network, where the rate a…