6 papers
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…
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…
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…
Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent
Philip Doldo, Derek Everett, Amol Khanna +2
Projected Gradient Descent (PGD) under the ball has become one of the defacto methods used in adversarial robustness evaluation for computer vision (CV) due to its relia…
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…
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection
Amol Khanna, Chenyi Ling, Derek Everett +2
Existing methods for out-of-distribution (OOD) detection use various techniques to produce a score, separate from classification, that determines how ``OOD'' an input is. Our insig…