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首页> 外文会议>International Conference on Robot Intelligence Technology and Applications >Go-Detect Application Inspired by Apoptosis to Detect SMS Exploitation by Malwares
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Go-Detect Application Inspired by Apoptosis to Detect SMS Exploitation by Malwares

机译：通过细胞凋亡激发的去检测应用，以检测恶意的短信剥削

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Nowadays, malware attack mobile phone compared to the computer due to its mobility and extensive usage. The users are being exposed with sophisticated threats that lead to loss of money and confidential information. These threats are inferred by malwares that exploit the mobile applications (apps) vulnerabilities. Five surveillance features in a mobile phone commonly used by the malwares includes Short Message Service (SMS), call log, Global Positioning System (GPS), camera and audio. This paper focuses on the SMS feature and presents a mobile apps called as Go-Detect inspired by Apoptosis to detect SMS exploitation by malwares. There are 16 new SMS Android Package Index (API) classifications that have been developed and used as the input for this app. Apoptosis or known as cell-programmed-death is a concept borrowed from human immunology that has been integrated in this app. It will uninstall and delete the infected apps that matched with the proposed classifications. A total of 5560 Drebin dataset has been used as the training dataset and reverse engineered using static analysis in a controlled lab environment. This app is built by using JAVA. Based on the testing conducted with 50 anonymous mobile apps from the Google Play store, 36% matched with the proposed classification. This new classification and apps can be used as the reference and basis for other researchers to detect malware in a mobile phone.
机译：如今，由于其移动性和广泛使用，恶意软件攻击手机与计算机相比。用户正在暴露出威胁的复杂威胁，导致货币和机密信息丢失。利用移动应用程序（应用程序）漏洞的恶意来推断出这些威胁。 Malwares通常使用的手机中的五种监视功能包括短消息服务（SMS），呼叫记录，全球定位系统（GPS），相机和音频。本文重点介绍了SMS功能，并提出了一个称为Go-Dread的移动应用，受到细胞凋亡的启发，以检测恶意恶魔的SMS开发。已开发出16个新的SMS Android Package索引（API）分类，并用作此应用的输入。细胞凋亡或称为细胞编程死亡是一种从本作这项应用程序融入的人类免疫学借来的概念。它将卸载并删除与所提出的分类匹配的受感染的应用程序。总共5560个DRebin数据集已被用作使用受控实验室环境中的静态分析的训练数据集和反向设计。此应用程序是通过使用Java构建的。根据使用Google Play商店的50个匿名移动应用程序进行的测试，36％与所提出的分类匹配。此新分类和应用程序可作为其他研究人员用作其他研究人员在手机中检测恶意软件的参考和依据。

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