.png){kind=icon}
.png){kind=icon}
Return
A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3551152.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The exponential growth of Android applications has resulted in a surge of malware threats, posing severe risks to user privacy and data security. To address these challenges, this study introduces a novel malware detection approach utilizing an ensemble of Convolutional Neural Networks (CNNs) for enhanced classification accuracy. The methodology incorporates a multi-phase process, starting with the extraction and preprocessing of APK (Android app) files. The preprocessing phase involves decompressing, decompiling, and transforming the APK files into bytecode and Dex files. The extracted byte data is converted into 1D vectors and reshaped into 2D grayscale images, enabling efficient feature learning through CNNs. The proposed ensemble of CNN-based models undergoes comprehensive training, validation, and evaluation, demonstrating superior performance compared to existing approaches. We used two popular Android datasets to evaluate the performance of our proposed model. Specifically, the model achieves an accuracy of 98.65%, F1-score of 96.43% on the Drebin dataset and attains 97.91% accuracy, 96.73% of F1-score on the AMD dataset. These results confirm the mode's ability to effectively identify Android malware with high precision and reliability, outperforming traditional techniques. This research not only underscores the potential of our proposed approach in malware detection but also sets a foundation for future advancements. Future efforts will focus on real-time malware detection, integration with mobile security frameworks, and evaluation across diverse datasets to ensure adaptability to emerging malware threats.
Keywords:
Malware
Accuracy
Feature extraction
Machine learning
Deep learning
Static analysis
Real-time systems
Random forests
Computational modeling
Support vector machines
Android malware detection
convolutional neural networks
malware classification
machine learning
ensemble learning
attention mechanism
Meta-CNN
deep learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
View more organizations