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Detect repackaged Android application based on HTTP traffic similarity

机译:根据HTTP流量相似性检测重新打包的Android应用程序

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In recent years, more and more malicious authors aim to Android platform because of the rapid growth number of Android (Google, Menlo Park, California, USA) applications (or apps). They embedded malicious code into Android apps to execute their special malicious behaviors, such as sending text messages to premium numbers, stealing privacy information, or even converting the infected phones into bots. We called the app, which has been embedded with malicious code, as embedded repackaged app. This phenomena leads a big security risk to the Android users and how to detect them becomes an urgent problem. Previous research efforts focus on extracting the app's characteristics for comparison from its static program code, which neither can handle the code obfuscation technologies, nor can analyze the app's dynamic behaviors feature. To address these limitations, we propose an approach based on extracting the app's characteristics from the HTTP traffic, which is generated by the app. Moreover, we have implemented a multi‐thread comparison algorithm based on the balanced Vantage Point Tree (VPT), which can remarkably reduce the experiment time. In this experiment, we successfully detected 266 embedded repackaged apps from 7619 Android apps downloaded from six popular Android markets, and the distribution rate of each market ranges from 2.57% to 6.07%. Then based on the analyzing of the HTTP traffic generated by these embedded codes, we found that majority of them are advertisement traffic and malicious traffic. Copyright ? 2015 John Wiley & Sons, Ltd. We call the app that has been embedded additional code as embedded repackaged app, which causes security issues to Android users. To address the existing approaches' limitations, we first capture and parse the traffic generated by apps, and classify them into primary and non‐primary module traffic set. Then we calculate the similarity of primary module and use the balanced vantage point tree comparison algorithm to detect them. Finally, we detected 266 embedded repackaged apps from 7619 Android apps.
机译:近年来,由于Android(Google,Menlo Park,美国加利福尼亚州)应用程序(或应用程序)的数量迅速增长,越来越多的恶意作者将目标锁定在Android平台上。他们将恶意代码嵌入Android应用程序中,以执行其特殊的恶意行为,例如将短信发送给高级号码,窃取隐私信息,甚至将受感染的手机转化为机器人。我们将已嵌入恶意代码的应用程序称为嵌入式重新打包的应用程序。这种现象给Android用户带来了很大的安全隐患,如何检测到他们已成为当务之急。先前的研究工作集中于从其静态程序代码中提取应用程序的特征以进行比较,这既不能处理代码混淆技术,也不能分析应用程序的动态行为特征。为了解决这些限制,我们提出了一种基于从应用程序生成的HTTP流量中提取应用程序特征的方法。此外,我们基于平衡的Vantage点树(VPT)实现了多线程比较算法,可以显着减少实验时间。在此实验中,我们成功从6个热门Android市场下载的7619个Android应用程序中成功检测到266个嵌入式重新打包的应用程序,每个市场的分布率在2.57%至6.07%之间。然后,在对这些嵌入式代码生成的HTTP流量进行分析的基础上,我们发现其中大多数是广告流量和恶意流量。版权? 2015 John Wiley&Sons,Ltd.我们将已嵌入其他代码的应用程序称为嵌入式重新打包的应用程序,这会给Android用户带来安全问题。为了解决现有方法的局限性,我们首先捕获并解析应用程序生成的流量,然后将其分类为主要和非主要模块流量集。然后,我们计算主模块的相似度,并使用平衡优势点树比较算法对其进行检测。最后,我们从7619个Android应用程序中检测到266个嵌入式重新打包的应用程序。

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