Analysis of Dataset Shifts in Mobile Malware

移动恶意软件中数据集变化的分析

• 批准号: 456292463
• 负责人: Dr.-Ing. Daniel Arp
• 金额: --
• 依托单位: Fachgebiet Maschinelles Lernen/ Intelligente Datenanalyse
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/456292463?language=en
• 关键词: Analysis; Dataset; Shifts; Mobile; Malware

The popularity of mobile devices, such as smartphones and tablets, has grown 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 cannot always 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 mobile malware based on machine learning. These approaches have proven to be capable of deriving effective patterns to detect malware automatically. Most recently, however, it has been shown that the detection performance of these methods decreases over time, a phenomenon referred to as "dataset shift" in machine learning theory. While, for instance, the growing use of obfuscation techniques in mobile applications explains parts of these observations, the causes for dataset shift in this domain are mostly still unknown.In this project, we aim to gather a comprehensive understanding of the reasons behind dataset shift and how to alleviate its impact on the detection performance of learning-based systems for mobile malware detection. To this end, we guide our research along the following two steps: In the first step, we analyze the detection capabilities of existing approaches over time by adapting methods for interpreting machine learning models. This way, we attempt to identify factors that impede the detection of mobile malware and are primarily responsible for the emergence of dataset shift. In the second step, we explore different feature spaces to develop suitable methods for detecting mobile malware over time.

在过去十年中，智能手机和平板电脑等移动设备的普及程度显著提高。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及时提供产品更新。为了弥补当前反病毒扫描器的弱点，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。这些方法已被证明能够产生有效的模式来自动检测恶意软件。然而，最近有研究表明，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部分原因，但该领域数据集移动的原因大多仍然未知。在这个项目中，我们的目标是全面了解数据集迁移背后的原因，以及如何减轻其对基于学习的移动恶意软件检测系统的检测性能的影响。为此，我们沿着以下两个步骤指导我们的研究：第一步，我们通过适应解释机器学习模型的方法来分析现有方法随时间的检测能力。通过这种方式，我们试图确定阻碍移动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，我们探索不同的特征空间，以开发适合的方法来检测移动恶意软件随时间的变化。