Android malware detection using stacked generalization

Malware detection plays a key role in Android device security due to the popularity of Android with billions of active users that encouraging cybercriminals to push the malware into this operating system. The growth of malware is now becoming a serious problem. Recently, extensive research has been conducted to detect malware on Android devices using machine learning based methods profoundly depending on domain knowledge for manually extracting malicious features. In this paper, we evaluate tree-based machine learning algorithms by Stacked Generalization concept for detecting malware on Android in conjunction with implementing a substring-based method for training the algorithms. We perform experiments on 11,120 samples containing 5,560 malware samples and 5,560 benign samples provided by DREBIN dataset on 8 malware families. The evaluation results show how stacked generalization achieves 97.92% validation accuracy for malware detection on DREBIN dataset.