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Survey on Android Malware Detection and Protection using Data Mining Algorithms

机译:基于数据挖掘算法的Android恶意软件检测与防护研究

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Mobile devices have become very popular these days due to its portability and high performance. Android occupies a majority share in the mobile market and it becomes trendy nowadays. The reason for user ignoring is that the installation and usage of apps. The user must have the concern about the security and the malicious attacks. Like every operating systems mobile devices are also prone to malware attacks. Several data mining algorithms are used in the malware detection based on the permissions enabled in each apps. This paper gives a n attempt to study about the performance of data mining algorithms such as Naive Bayes, J48, Multiclass Classifier, Random Tree, SVM, Decision Tree. Each and every algorithm is assessed by various criteria to identify which one is suitable to detect malicious software. The result is that Naive Bayes if far better than the other algorithms as of in time concern and also in detection process.
机译:由于其便携性和高性能,近来移动设备变得非常流行。 Android占据了移动市场的大部分份额,并且如今已成为时尚。用户忽略的原因是应用程序的安装和使用。用户必须关注安全性和恶意攻击。像每个操作系统一样,移动设备也容易受到恶意软件的攻击。根据每个应用程序中启用的权限,几种数据挖掘算法用于恶意软件检测。本文尝试研究朴素贝叶斯,J48,多类分类器,随机树,SVM,决策树等数据挖掘算法的性能。每种算法均通过各种标准进行评估,以识别哪种算法适合检测恶意软件。结果是,就时间而言以及在检测过程中,朴素贝叶斯要远胜于其他算法。

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