SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning
This work addresses the challenge of unreliable datasets in Android malware detection, offering a solution that is less dependent on data quality, though it appears incremental in nature.
The authors tackled the problem of Android malware detection by proposing SEdroid, a selective ensemble learning approach that uses a genetic algorithm to combine component learners, achieving more robust performance than other methods.
For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the quality of datasets, and perform unsatisfactory results when the quality of training data is not good enough. In the real world, the quality of datasets without manually check cannot be guaranteed, even Google Play may contain malicious applications, which will cause the trained model failure. To address the challenge, we propose a robust Android malware detection approach based on selective ensemble learning, trying to provide an effective solution not that limited to the quality of datasets. The proposed model utilizes genetic algorithm to help find the best combination of the component learners and improve robustness of the model. Our results show that the proposed approach achieves a more robust performance than other approaches in the same area.