101 citations · 195 across the 7 of their papers we have counts for
9 papers
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…
A Language-Agnostic Model for Semantic Source Code Labeling
Ben Gelman, Bryan Hoyle, Jessica Moore +2
Code search and comprehension have become more difficult in recent years due to the rapid expansion of available source code. Current tools lack a way to label arbitrary code at sc…
MEADE: Towards a Malicious Email Attachment Detection Engine
Ethan M. Rudd, Richard Harang, Joshua Saxe
Malicious email attachments are a growing delivery vector for malware. While machine learning has been successfully applied to portable executable (PE) malware detection, we ask, c…
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content
Joshua Saxe, Richard Harang, Cody Wild +1
Malicious web content is a serious problem on the Internet today. In this paper we propose a deep learning approach to detecting malevolent web pages. While past work on web conten…
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys
Joshua Saxe, Konstantin Berlin
For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack. Unfortunately,…
Improving Zero-Day Malware Testing Methodology Using Statistically Significant Time-Lagged Test Samples
Konstantin Berlin, Joshua Saxe
Enterprise networks are in constant danger of being breached by cyber-attackers, but making the decision about what security tools to deploy to mitigate this risk requires carefull…