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Mobile botnet detection: Proof of concept

机译:移动僵尸网络检测:概念证明

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Nowadays mobile devices such as smartphones had widely been used. People use smartphones not limited for phone calling or sending messages but also for web browsing, social networking and online banking transaction. To certain extend, all confidential information are kept in their smartphone. As a result, smartphones became as one of the cyber-criminal main target especially through an installation of mobile botnet. Eurograbber is an example of mobile botnet that being installed via infected mobile application without victim knowledge. It will pretense as mobile banking application software and steal financial transaction information from victim's smartphone. In 2012, Eurograbber had caused a total loss of USD 47 Million accumulatively all over the world. Based on the implications posed by this botnet, this is the urge where this research comes in. This paper presents a proof of concept on how the botnet works and the ongoing research to detect and respond to the mobile botnet efficiently. Detection of botnet malicious activity is done through an analysis of Crusewind Botnet code using reverse engineering process and static analysis technique.
机译:如今,诸如智能手机的移动设备已被广泛使用。人们使用智能手机不限于电话或发送消息,而且还用于网络浏览,社交网络和网上银行交易。对于某些扩展,所有机密信息都保存在智能手机中。因此,智能手机成为网络犯罪主要目标之一,特别是通过安装移动僵尸网络。 EuroGrabber是通过没有受害者知识的受感染的移动应用程序安装的移动僵尸网络的示例。它将作为移动银行申请软件和受害者智能手机窃取金融交易信息的假装。 2012年,欧元造厂造成了全世界累计4700万美元的总损失。基于本僵尸网络提出的影响,这是该研究进入的敦促。本文介绍了僵尸网络如何运作和持续研究以有效地检测和响应移动僵尸网络的概念证明。通过使用逆向工程过程和静态分析技术分析Crusewind僵尸网络代码来检测僵尸网络恶意活动。

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