DRoid分析师组合一个android恶意软件分析框架

S. Bhandari, Rishabh Gupta, V. Laxmi, M. Gaur, A. Zemmari, M. Anikeev
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引用次数: 38

摘要

Android作为最流行的开源移动操作系统,吸引了大量的应用开发者。数以百万计的应用程序是针对Android平台开发的,具有很大程度的行为多样性,并且可以在Play Store以及许多第三方应用商店中使用。由于其开放性,Android平台在过去已经成为许多恶意软件编写者的目标。由于同一应用程序具有不同签名的变体数量呈指数级增长,传统的基于签名的检测方法用于检测设备上的恶意软件已不再有希望。此外,它们也缺乏动态分析。在本文中,我们提出了DRACO,它采用了两相检测技术,混合了静态和动态分析的协同作用。它有两个模块,客户端模块以Android应用程序的形式安装在移动设备上,服务器模块运行在服务器上。DRACO还向用户解释了导致分析应用程序恶意的功能,并为该恶意生成评分。它不需要任何root或超级用户权限。在对18,000个良性应用程序和10,000个恶意软件样本的评估中,DRACO的表现优于几种相关的现有方法,并且检测出98.4%的恶意软件,并且很少有错误警报。在十种流行的智能手机上,该方法平均需要6秒进行设备分析,90秒进行服务器分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DRACO: DRoid analyst combo an android malware analysis framework
Android being the most popular open source mobile operating system, attracts a plethora of app developers. Millions of applications are developed for Android platform with a great extent of behavioral diversities and are available on Play Store as well as on many third party app stores. Due to its open nature, in the past Android Platform has been targeted by many malware writers. The conventional way of signature-based detection methods for detecting malware on a device are no longer promising due to an exponential increase in the number of variants of the same application with different signatures. Moreover, they lack in dynamic analysis too. In this paper, we propose DRACO, which employs a two-phase detection technique that blends the synergy of both static and dynamic analysis. It has two modules, client module that is in the form an Android app and gets installed on mobile devices and a server module that runs on a server. DRACO also explains user about the features contributing to the maliciousness of analyzed app and generates scoring for that maliciousness. It does not require any root or super-user privileges. In an evaluation of 18,000 benign applications and 10,000 malware samples, DRACO performs better than several related existing approaches and detects 98.4% of the malware with few false alerts. On ten popular smartphones, the method requires an average of 6 seconds for on device analysis and 90 seconds on server analysis.
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