5 papers
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…
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…
Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
David Košťál, Martin JureÄek
We present a dataset of adversarial malware samples derived from the public RawMal-TF collection of real-world malware binaries. Using a suite of adversarial malware generators, we…
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
Jan DolejÅ¡, Martin JureÄek, Róbert Lórencz
Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work investigates a realistic gray-box p…
RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David Bálik, Martin JureÄek, 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…