基于机器学习的Android恶意应用检测

Hritik Soni, Pranjal Arora, D. Rajeswari
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引用次数: 9

摘要

最近,高级移动电话的使用正在不断扩大,而且Android应用程序客户端的开发也在扩大。由于Android应用客户端的发展,一些不法分子利用恶意的Android应用作为工具,获取敏感的信息和数据,进行欺诈和伪造。有大量的恶意应用程序发现工具和编程是可访问的。尽管如此,一种可行且高效的报复性应用程序识别设备有望处理和处理由入侵者或程序员制作的新的复杂有害应用程序。本文利用机器学习方法对恶意android应用程序进行识别。首先,需要借助帮助向量机计算和选择树计算来获得过去有害应用的数据集,并与准备数据集进行关联。所准备的数据集可以预见到android应用中高达93.2%的模糊/新恶意移动应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Malicious Application Detection in Android using Machine Learning
As of late, the uses of advanced mobile phones are expanding relentlessly and furthermore development of Android application clients are expanding. Because of development of Android application client, some gatecrashers are making vindictive android application as instrument to take the delicate information and data for fraud and misrepresentation portable bank, versatile wallets. There are such a large number of malevolent applications discovery instruments and programming’s are accessible. Be that as it may, a viably and productively vindictive application recognition device expected to handle and deal with new complex pernicious applications made by interloper or programmers. This paper Utilizing Machine Learning approaches for distinguishing the malignant android application. First, dataset of past pernicious applications has to be obtained with the assistance of Help vector machine calculation and choice tree calculation make up correlation with preparing dataset. The prepared dataset can foresee the malware android applications up to 93.2 % obscure/new malware portable application.
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