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首页> 外文期刊>International journal of ambient computing and intelligence >Malicious Application Detection and Classification System for Android Mobiles
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Malicious Application Detection and Classification System for Android Mobiles

机译：Android手机的恶意应用程序检测和分类系统

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摘要

>The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.
机译：> Android手机在移动市场中占有很大份额，这也吸引了恶意软件开发者以获取恶意收益。每年，Android市场中都会检测到数百种恶意软件。诸如Google Play商店这样的非官方和官方Android市场上充斥着假冒和恶意应用程序，这对天真的用户是一个警告。以此见识为指导，本文通过提取和分析Android应用程序的Android许可功能，介绍了使用机器学习技术的恶意应用程序检测和分类系统。对于特征提取，这项工作的作者开发了用Shell脚本编写的AndroData工具，并使用机器学习算法分析了1060个Android应用程序的提取特征。通过机器学习技术，它们分别实现了98.2％和87.3％的恶意应用程序检测和分类准确率。

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