As the number of Android malware applications continues to grow at a high rate, detecting malware to protect the system security and user privacy is becoming increasingly urgent. Each malware application belongs to a specific family, and there is a gap in the number of malware families. The accuracy of detection can be improved if malware family information is well utilized and certain strategies are adopted to balance the variability among samples. In addition, the performance of a base classifier is limited. If an ensemble classifier or an ensemble method can be adopted, the detection effect can be further improved. Therefore, this paper proposes a novel malware family-based bagging algorithm for Android malware detection, called FB2Droid, to perform malware detection. First, five features are extracted from the Android application package. Then, the relief feature selection algorithm is used for feature selection. Next, we designed two different sampling strategies based on different families of malware to alleviate the sample imbalance in the dataset. Combined with the two sampling strategies, the traditional bagging algorithm is improved to integrate the classifier. In the experiment, several classifiers were used to evaluate the proposed scheme. The experimental results show that the proposed sampling strategy and the improved bagging algorithm can effectively improve the detection accuracy of these classifiers.

中文翻译：

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FB2Droid：一种用于 Android 恶意软件检测的基于恶意软件家族的新型 Bagging 算法

随着Android恶意软件应用的数量持续高速增长，检测恶意软件以保护系统安全和用户隐私变得越来越紧迫。每个恶意软件应用程序都属于一个特定的家族，并且恶意软件家族的数量存在差距。如果充分利用恶意软件家族信息并采用某些策略来平衡样本之间的可变性，则可以提高检测的准确性。此外，基分类器的性能是有限的。如果可以采用集成分类器或集成方法，可以进一步提高检测效果。因此，本文提出了一种新的基于恶意软件家族的装袋算法，用于 Android 恶意软件检测，称为 FB2Droid，用于执行恶意软件检测。首先，从Android应用程序包中提取五个特征。然后，使用浮雕特征选择算法进行特征选择。接下来，我们根据不同的恶意软件家族设计了两种不同的采样策略，以缓解数据集中的样本不平衡。结合两种采样策略，对传统的bagging算法进行了改进，将分类器集成在一起。在实验中，使用了几个分类器来评估所提出的方案。实验结果表明，所提出的采样策略和改进的bagging算法能够有效提高这些分类器的检测精度。对传统的bagging算法进行了改进，集成了分类器。在实验中，使用了几个分类器来评估所提出的方案。实验结果表明，所提出的采样策略和改进的bagging算法能够有效提高这些分类器的检测精度。对传统的bagging算法进行了改进，集成了分类器。在实验中，使用了几个分类器来评估所提出的方案。实验结果表明，所提出的采样策略和改进的bagging算法能够有效提高这些分类器的检测精度。