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: 456292433
• 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 Position
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/456292433?language=en
• 关键词: Analysis; Dataset; Shifts; Mobile; Malware

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.

过去十年中，智能手机和平板电脑等移动设备的普及率显着增长。不幸的是，它们的流行也使它们成为恶意软件作者的盈利目标，导致这些设备经常得不到保护，因为防病毒供应商不能总是按时为其产品提供更新。为了弥补当前防病毒扫描程序的弱点，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。事实证明，这些方法能够导出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“数据集转移”。例如，虽然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内容，但该领域数据集转移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转移背后的原因，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，我们按照以下两个步骤指导我们的研究：第一步，我们通过调整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，我们尝试找出阻碍移动恶意软件检测以及导致数据集转移出现的主要原因的因素。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随着时间的推移检测移动恶意软件。