3 papers
cs.CR2025
IoTEdu: Access Control, Detection, and Automatic Incident Response in Academic IoT Networks
Joner Assolin, Diego Kreutz, Leandro Bertholdo
The growing presence of IoT devices in academic environments has increased operational complexity and exposed security weaknesses, especially in academic institutions without unifi…
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.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…