563 citations
- DEVCOM Army Research LaboratoryUS9 papers
- University of Maryland, Baltimore CountyUS7 papers
- George Mason UniversityUS5 papers
- Physical Sciences (United States)US5 papers
- Stanford UniversityUS5 papers
- University of California, BerkeleyUS4 papers
- University of Maryland, College ParkUS4 papers
- Cornell UniversityUS3 papers
- Harvard UniversityUS3 papers
- Massachusetts Institute of TechnologyUS3 papers
- United States Naval ObservatoryUS3 papers
- Université Bourgogne Franche-ComtéFR3 papers
66 papers
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…
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms
Derek Everett, Fred Lu, Edward Raff +2
Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed…
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…
Verifying Energy-Time Entanglement with Irregularly Sampled Correlations
James Schneeloch, Christopher C. Tison, Richard J. Birrittella +3
Verifying entanglement with experimental measurements requires that we take the limitations of experimental techniques into account, while still proving that the data obtained coul…
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
Fred Lu, Ryan R. Curtin, Edward Raff +2
As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and expl…