4 citations · 4 across the 5 of their papers we have counts for
5 papers
Synthetic Data: AI's New Weapon Against Android Malware
Angelo Gaspar Diniz Nogueira, Kayua Oleques Paim, Hendrio Bragança +2
The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the critical importance of effective det…
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…
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…
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…
Vibration Sensor Dataset for Estimating Fan Coil Motor Health
Heitor Lifsitch, Gabriel Rocha, Hendrio Bragança +4
To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely pos…