  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.
