3 papers
cs.CR2025
MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research
Hendrio Braganca, Diego Kreutz, Vanderson Rocha +2
We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide ran…
cs.LG2025
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation
Vanderson Rocha, Diego Kreutz, Gabriel Canto +2
Feature selection is vital for building effective predictive models, as it reduces dimensionality and emphasizes key features. However, current research often suffers from limited…
cs.CR2025
Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Joner Assolin, Gabriel Canto, Diego Kreutz +4
Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to s…