基于数据挖掘算法的Android恶意软件检测与防护研究综述

Q3 Medicine
K. Uma, E. S. Blessie
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引用次数: 5

摘要

移动设备由于其便携性和高性能而变得非常流行。Android在手机市场占有很大份额,成为时下的潮流。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。与所有操作系统一样,移动设备也容易受到恶意软件的攻击。基于每个应用程序中启用的权限,在恶意软件检测中使用了几种数据挖掘算法。本文尝试对朴素贝叶斯、J48、多类分类器、随机树、支持向量机、决策树等数据挖掘算法的性能进行了研究。每一种算法都通过各种标准进行评估,以确定哪一种算法适合检测恶意软件。结果表明,无论是在时间关注上还是在检测过程上,朴素贝叶斯算法都远远优于其他算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survey on Android Malware Detection and Protection using Data Mining Algorithms
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.
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来源期刊
Koomesh
Koomesh Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
0
审稿时长
24 weeks
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