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Android Malware Classification Approach Based on Host-Level Encrypted Traffic Shaping

机译：基于主机级加密流量整形的Android恶意软件分类方法

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摘要

With the development of mobile terminals, smartphones have attracted a very huge number of users with their powerful functions. Among them, Android system is famous for its open-source and convenience, which occupies a large market share. But this also leads many attackers to use their malware to gain benefits quickly, which make it necessary to design a practical android malware detection approach. At present, there are not many pieces of research on detecting malware by analyzing Android malicious traffic. This paper examines the characteristics of malicious traffic on the host computer to construct a traffic fingerprint. It combines machine learning algorithms to build a practical detection approach which is also suitable for encrypted traffic. To distinguish similar fuzzy traffic, an additional layer named confusion classifier is added to help further malware classification. This paper uses a realworld dataset called CICAndMal2017 and simulates two classification scenarios: malware binary detection and malware category classification. The experimental results show that the accuracy of the malware binary detection reached 98.8% while the accuracy rate of malware category classification is 95.2%.
机译：随着移动终端的发展，智能手机吸引了具有强大功能的非常大量的用户。其中，Android系统以其开源和便利而闻名，占据了大量的市场份额。但这也导致许多攻击者使用恶意软件快速获得福利，这使得有必要设计一个实用的Android恶意软件检测方法。目前，通过分析Android恶意流量，没有许多关于检测恶意软件的研究。本文介绍了主机上的恶意流量的特征，以构建流量指纹。它结合了机器学习算法来构建一种实用的检测方法，该方法也适用于加密流量。为了区分类似的模糊流量，添加了一个名为Conumivion Classifier的附加层以帮助进一步恶意软件分类。本文使用名为Cicandmal2017的RealWorld数据集，并模拟两个分类方案：恶意软件二进制检测和恶意软件类别分类。实验结果表明，恶意软件二进制检测的准确性达到98.8％，而恶意软件类别分类的准确率为95.2％。

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