27 citations · 57 across the 6 of their papers we have counts for
7 papers
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
Robert J. Joyce, Gideon Miller, Phil Roth +5
A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing…
Is Function Similarity Over-Engineered? Building a Benchmark
Rebecca Saul, Chang Liu, Noah Fleischmann +4
Binary analysis is a core component of many critical security tasks, including reverse engineering, malware analysis, and vulnerability detection. Manual analysis is often time-con…
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection
Edward Raff, William Fleshman, Richard Zak +3
Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengt…
Automatic Yara Rule Generation Using Biclustering
Edward Raff, Richard Zak, Gary Lopez Munoz +5
Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-in…
KiloGrams: Very Large N-Grams for Malware Classification
Edward Raff, William Fleming, Richard Zak +4
N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of are tested, with $n…
RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement
Aditya Pingle, Aritran Piplai, Sudip Mittal +3
Security Analysts that work in a `Security Operations Center' (SoC) play a major role in ensuring the security of the organization. The amount of background knowledge they have abo…
Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
William Fleshman, Edward Raff, Richard Zak +2
As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) s…