27 citations · 81 across the 32 of their papers we have counts for
12 papers · 1 filter
Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes
Derek Everett, Edward Raff, James Holt
Malware poses a critical and ever-evolving threat, and robust and effective systems for detecting and classifying malware are of essential importance. -grams features are among…
ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage
Chang Liu, Noah Fleischmann, Nicolò Altamura +3
Existing binary corpora typically capture only one or two axes of binary variation: they either provide cross-compiler builds without a temporal axis, or CVE labels for single-buil…
Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora
Edward Raff, Ryan R. Curtin, Derek Everett +2
A classifier using byte n-grams as features is the only approach we have found fast enough to meet requirements in size (sub 2 MB), speed (multiple GB/s), and latency (sub 10 ms) f…
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…
ClarAVy: A Tool for Scalable and Accurate Malware Family Labeling
Robert J. Joyce, Derek Everett, Maya Fuchs +2
Determining the family to which a malicious file belongs is an essential component of cyberattack investigation, attribution, and remediation. Performing this task manually is time…
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Siddhant Gupta, Fred Lu, Andrew Barlow +5
A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious acto…