3 papers
cs.LG2026
Concept Drift Detection and Adaptive Retraining of Malware Classification Models
Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika +2
Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malwar…
cs.CV2026
CAM-Guided Saliency Cutout and Image-Based Malware Classification
Yasaman Ebrahimi, Martin Jurecek, Mark Stamp
Dropout regularization is commonly used to reduce overfitting by removing parts of a neural network during training. For Convolutional Neural Networks (CNN), cutouts serve a somewh…
cs.CR2025
RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David Bálik, Martin Jureček, Mark Stamp
This work addresses the challenge of malware classification using machine learning by developing a novel dataset labeled at both the malware type and family levels. Raw binaries we…