Malware Detection with Obfuscation Techniques on Android Using Dynamic Analysis

T. Mantoro, Daniel Stephen, Wandy Wandy
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Abstract

Android malware has become a growing issue for Android users, thanks to the popularity of the Operating System that has grown rapidly throughout the decade. Various approaches have been implemented to do malware detection in the Android OS, such as machine learning algorithms for heuristic approaches and frameworks to improve the detection and classification of malware. Unfortunately, most of the antivirus application that is available in the Android operating system is fully based on static methods, which cannot detect malware that used obfuscation techniques. This paper used the Mobile Security Framework by using the dynamic analysis method to detect malware with obfuscation techniques. The framework’s dynamic analysis method will be used to analyze multiple malware. The study aims to demonstrate dynamic analysis and measure the effectiveness of using dynamic analysis in detecting various malware. The method managed to detect 3 out of 7 malware applications that are present in the dataset. Several factors might contribute to the performance of the method, such as hardware limitations, obsolete application versions, and the ability of the application to behave differently with emulators.
基于动态分析的Android上混淆技术恶意软件检测
Android恶意软件对Android用户来说已经成为一个日益严重的问题,这要归功于过去十年中快速增长的操作系统的普及。在Android操作系统中,已经实现了各种方法来进行恶意软件检测,例如用于启发式方法的机器学习算法和框架,以改进恶意软件的检测和分类。不幸的是,Android操作系统中可用的大多数防病毒应用程序完全基于静态方法,无法检测使用混淆技术的恶意软件。本文采用移动安全框架,采用动态分析方法对恶意软件进行混淆检测。该框架的动态分析方法将用于分析多个恶意软件。本研究旨在演示动态分析,并测量使用动态分析检测各种恶意软件的有效性。该方法成功检测出数据集中存在的7个恶意软件应用程序中的3个。有几个因素可能会影响该方法的性能,例如硬件限制、过时的应用程序版本,以及应用程序在使用模拟器时表现不同的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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