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Intelligent malware detection on Android smartphones via a hybrid approach using gradient boosting and convolutional
Collins Chimeleze1,2, Norziana Jamil3, Zuhaira Muhammad Zain4
1Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Selangor, Malaysia. chimeleze@uniten.edu.my.
Scientific Reports
|August 6, 2026
Summary
This study introduces CNN-GBM, a hybrid model for Android malware detection. It significantly improves accuracy and reduces error rates compared to existing deep learning methods.
Area of Science:
- Computer Science
- Cybersecurity
- Machine Learning
Background:
- Malware detection is crucial for system security.
- Existing machine learning methods often require extensive feature engineering, increasing computation time and errors.
- Deep learning models show promise but can be computationally intensive.
Purpose of the Study:
- To propose a novel hybrid model, CNN-GBM, for enhanced Android malware detection.
- To overcome limitations of existing methods by reducing feature engineering demands and improving efficiency.
- To improve the accuracy and reduce error rates in identifying malicious software.
Main Methods:
- A hybrid model combining Convolutional Neural Networks (CNN) and Gradient Boosted Machines (GBM) was developed.
- Data was represented using n-grams, opcode sequences, or byte-level features.
- CNN extracted hierarchical patterns, followed by entropy-based feature selection and GBM classification.
Main Results:
- CNN-GBM demonstrated superior performance over existing deep learning models.
- Achieved notable improvements: +3% accuracy, +1% precision, +5% recall, +3.5% AUC.
- Significantly reduced false positive rate (-52%), false negative rate (-15%), and error rate (-35%).
Conclusions:
- The CNN-GBM hybrid model offers a more effective approach to Android malware detection.
- Integration of CNN's feature extraction with GBM's boosting capabilities enhances detection performance.
- This method presents a promising direction for developing efficient and accurate malware detection systems.