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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.CR2026
A Comparison of Malware Image Transformations Using Grad-CAM and Hybrid Learning Models
Vibha Bhavikatti, Mark Stamp
Recent studies have shown that binary-to-image representations can enable effective machine learning-based results for malware detection and classification. However, performance ca…
cs.CV2026
Robustness of AI-Art Detectors under Generator Shift
Shivank Singh Thakur, Meien Li, Mark Stamp
Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-cr…