Context:

In last decade, due to tremendous usage of smart phones it seems that these gadgets became an essential necessity of day-to-day life. People are using new technologies and storing prominent data in their smartphones. Unfortunately, data related to privacy is center of attraction for attackers. Therefore, attackers are developing new techniques to steal the data from smartphones.

Objective:

The objective of study is to report a systematic literature review regarding malicious application detection in android operating system.

Method:

Standard systematic literature review method is used to carry out the research. In this, 380 research articles are studied which are published in various prominent international journals and conferences.

Results:

The different techniques which are used to investigate malicious application are identified. Furthermore, features used in static and dynamic technique are classified according to their usage in recent approaches. Various hybrid methods are analyzed and mapped according to the combination of static and dynamic features used. A variety of machine learning techniques are also identified and categorized in different classes. The datasets are listed are taken from various previous research approaches.

Conclusion:

This research will help to identify malicious applications in android operating system. New hybrid techniques must be implemented to investigate malware activities and recommendations are given for future research.

中文翻译：

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android中的恶意应用程序检测-系统文献综述

语境：

在过去的十年中，由于智能手机的大量使用，这些小工具似乎已成为日常生活的必需品。人们正在使用新技术并将重要数据存储在智能手机中。不幸的是，与隐私相关的数据是攻击者的吸引力中心。因此，攻击者正在开发新技术来窃取智能手机中的数据。

客观的：

研究的目的是报告有关android操作系统中恶意应用程序检测的系统文献综述。

方法：

采用标准的系统文献综述方法进行研究。在本文中，研究了380篇研究文章，这些文章发表在各种著名的国际期刊和会议上。

结果：

确定了用于调查恶意应用程序的不同技术。此外，在静态和动态技术中使用的特征根据它们在最近的方法中的用途进行分类。根据使用的静态和动态特征的组合，分析和映射了各种混合方法。还可以识别各种机器学习技术并将其分类为不同的类别。列出的数据集取自以前的各种研究方法。

结论：

这项研究将有助于识别android操作系统中的恶意应用程序。必须采用新的混合技​​术来调查恶意软件活动，并提出建议以供将来研究。