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cs.CR2026
Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
Tomáš Kalný, Martin JureÄek, Mark Stamp
This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the E…
cs.CR2025
A Comparison of Selected Image Transformation Techniques for Malware Classification
Rishit Agrawal, Kunal Bhatnagar, Andrew Do +2
Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, b…