66 citations · 104 across the 5 of their papers we have counts for
9 papers · 1 filter
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…
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…