With the rapid growth of Android applications, there is an urgent need for powerful Android malware detection technology nowadays. Existing classification models can be summarized with the following two steps-feature extraction and classification model learning. To further enhance the representation ability of existing classification models, this article presents an Android malicious application detection framework termed multiview information integration technology (MVIIDroid). To be specific, in our approach, we extract applications’ multiple components, transform them into embedding feature vectors and train a multiple Kernel learning model as the classifier. To illustrate the effectiveness of our model, we evaluate MVIIDroid on two Android malware datasets of 6820 malware and 6820 benign applications. Results show that we have superior classification performances when separating malware from benign applications. Moreover, we further evaluate MVIIDroid's ability to attribute malicious applications to their actual families. The experimental results well demonstrate the effectiveness of the proposed model.

中文翻译：

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MVIIDroid：Android恶意软件检测和家族识别的多视图信息集成方法

随着Android应用程序的快速增长，当今迫切需要功能强大的Android恶意软件检测技术。现有的分类模型可以通过以下两个步骤进行总结：特征提取和分类模型学习。为了进一步增强现有分类模型的表示能力，本文提出了一种称为多视图信息集成技术（MVIIDroid）的Android恶意应用程序检测框架。具体来说，在我们的方法中，我们提取应用程序的多个组件，将它们转换为嵌入特征向量，并训练多个内核学习模型作为分类器。为了说明我们模型的有效性，我们在6820个恶意软件和6820个良性应用程序的两个Android恶意软件数据集上评估了MVIIDroid。结果表明，当将恶意软件与良性应用程序分离时，我们具有出色的分类性能。此外，我们进一步评估了MVIIDroid将恶意应用程序归因于其实际家族的能力。实验结果很好地证明了所提模型的有效性。