DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection
提出一种多级架构的分类器融合方法DroidFusion，通过底层训练基分类器、高层用排序算法整合预测精度，提升安卓恶意软件检测准确率，实验表明其优于堆叠泛化方法。
Android malware has continued to grow in volume and complexity posing significant threats to the security of mobile devices and the services they enable. This has prompted increasing interest in employing machine learning to improve Android malware detection. In this paper, we present a novel classifier fusion approach based on a multilevel architecture that enables effective combination of machine learning algorithms for improved accuracy. The framework (called DroidFusion), generates a model by training base classifiers at a lower level and then applies a set of ranking-based algorithms on their predictive accuracies at the higher level in order to derive a final classifier. The induced multilevel DroidFusion model can then be utilized as an improved accuracy predictor for Android malware detection. We present experimental results on four separate datasets to demonstrate the effectiveness of our proposed approach. Furthermore, we demonstrate that the DroidFusion method can also effectively enable the fusion of ensemble learning algorithms for improved accuracy. Finally, we show that the prediction accuracy of DroidFusion, despite only utilizing a computational approach in the higher level, can outperform stacked generalization, a well-known classifier fusion method that employs a meta-classifier approach in its higher level.