27 citations · 81 across the 32 of their papers we have counts for
35 papers
Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets
Edward Raff, James Holt
Linear models with an -norm penalty remain state-of-the-art for high-dimensional () tasks, offering a straightforward method for solving real-world industry pro…
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…
A Vector Symbolic Approach to Multiple Instance Learning
Ehsan Ahmed Dhrubo, Mohammad Mahmudul Alam, Edward Raff +2
Multiple Instance Learning (MIL) tasks impose a strict logical constraint: a bag is labeled positive if and only if at least one instance within it is positive. While this iff cons…
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…