Analysis of Dataset Shifts in Mobile Malware

智能手机和平板电脑等移动的设备的普及在过去十年中显著增长。不幸的是，它们的流行也使它们成为恶意软件作者的有利可图的目标，使这些设备通常不受保护，因为反病毒供应商不能总是为他们的产品提供更新。为了弥补当前反病毒扫描仪的弱点，研究人员提出了各种基于机器学习的移动恶意软件检测方法。这些方法已被证明是能够获得有效的模式来自动检测恶意软件。然而，最近的研究表明，这些方法的检测性能会随着时间的推移而下降，这种现象在机器学习理论中被称为“数据集移位”。虽然，例如，在移动的应用程序中越来越多地使用混淆技术解释了这些观察结果的一部分，但在这个领域中数据集转移的原因仍然是未知的。在这个项目中，我们的目标是全面了解数据集转移背后的原因，以及如何减轻其对移动的恶意软件检测的基于学习的系统的检测性能的影响。为此，我们引导我们的研究沿着以下两个步骤：在第一步中，我们分析现有方法的检测能力随着时间的推移，通过调整方法来解释机器学习模型。通过这种方式，我们试图确定阻碍移动的恶意软件检测的因素，并主要负责数据集转移的出现。在第二步中，我们探索不同的特征空间，以开发合适的方法来检测移动恶意软件。

The popularity of mobile devices, such as smartphones and tablets, hasgrown significantly in the past decade. Unfortunately, theirpopularity has also made them a profitable target for malware authors,leaving these devices often unprotected, as anti-virus vendors cannotalways provide updates for their products on time.To compensate for the weaknesses of current anti-virus scanners,researchers have proposed various methods for the detection of mobilemalware based on machine learning. These approaches have proven to becapable of deriving effective patterns to detect malwareautomatically. Most recently, however, it has been shown that thedetection performance of these methods decreases over time, aphenomenon referred to as "dataset shift" in machine learningtheory. While, for instance, the growing use of obfuscation techniquesin mobile applications explains parts of these observations, thecauses for dataset shift in this domain are mostly still unknown.In this project, we aim to gather a comprehensive understanding of thereasons behind dataset shift and how to alleviate its impact on thedetection performance of learning-based systems for mobile malwaredetection. To this end, we guide our research along the following twosteps: In the first step, we analyze the detection capabilities ofexisting approaches over time by adapting methods for interpretingmachine learning models. This way, we attempt to identify factors thatimpede the detection of mobile malware and are primarily responsiblefor the emergence of dataset shift. In the second step, we exploredifferent feature spaces to develop suitable methods for detectingmobile malware over time.