PIndroid: A novel Android malware detection system using ensemble learning methods

Fauzia Idrees; Muttukrishnan Rajarajan; Mauro Conti; Thomas M. Chen; Yogachandran Rahulamathavan

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PIndroid: A novel Android malware detection system using ensemble learning methods

机译：PIndroid：使用集成学习方法的新型Android恶意软件检测系统

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The extensive use of smartphones has been a major driving force behind a drastic increase of malware attacks. Covert techniques used by the malware make them hard to detect with signature based methods. In this paper, we present PIndroid - a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with Ensemble methods for accurate malware detection. The proposed approach, when applied to 1,745 real world applications, provides 99.8% accuracy (which is best reported to date). Empirical results suggest that the proposed framework is effective in detection of malware apps.

机译：智能手机的广泛使用已成为恶意软件攻击急剧增加的主要推动力。恶意软件使用的隐蔽技术使它们很难使用基于签名的方法进行检测。在本文中，我们介绍了PIndroid-一种新颖的基于权限和意图的框架，用于识别Android恶意软件应用。据我们所知，PIndroid是第一个使用权限和意图相结合并结合Ensemble方法进行准确恶意软件检测的解决方案。所建议的方法应用于1745个实际应用程序时，可提供99.8％的准确性（迄今为止最好的报告）。实证结果表明，该框架可有效检测恶意软件。

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来源
《Computers & Security》 |2017年第7期|36-46|共11页
作者
Fauzia Idrees; Muttukrishnan Rajarajan; Mauro Conti; Thomas M. Chen; Yogachandran Rahulamathavan;
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作者单位

School of Mathematics & Engineering, City University London, London EC1V OHB, UK;

School of Mathematics & Engineering, City University London, London EC1V OHB, UK;

Department of Mathematics, University of Padua, 35122 Padova, Italy;

School of Mathematics & Engineering, City University London, London EC1V OHB, UK;

Institute for Digital Technologies, Loughborough University in London, London, UK;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Malware classification; Permissions; Intents; Ensemble methods; Colluding applications;

机译：恶意软件分类;权限;意图;合奏方法;合谋申请;

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1. Optimizing Android Malware Detection Via Ensemble Learning [J] . Abikoye Oluwakemi Christianah, Benjamin Aruwa Gyunka, Akande Noah Oluwatobi International Journal of Interactive Mobile Technologies . 2020,第09期

机译：通过集合学习优化Android恶意软件检测
2. Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection [J] . Md. Shohel Rana, Andrew H. Sung Vietnam Journal of Computer Science . 2020,第2期

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3. High accuracy android malware detection using ensemble learning [J] . Yerima Suleiman Y., Sezer Sakir, Muttik Igor Information Security, IET . 2015,第6期

机译：使用集成学习进行高精度Android恶意软件检测
4. Android Malware Detection using Chi-Square Feature Selection and Ensemble Learning Method [C] . Meghna Dhalaria, Ekta Gandotra International Conference on Parallel, Distributed and Grid Computing . 2020

机译：Android Malware检测使用Chi-Square特征选择和集合学习方法
5. Local parametric density-based outlier detection and ensemble learning with applications to malware detection. [D] . Williams, Kristopher T. 2016

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6. Adversarial Samples on Android Malware Detection Systems for IoT Systems [O] . Xiaolei Liu, Xiaojiang Du, Xiaosong Zhang, 2019

机译：用于物联网系统的Android恶意软件检测系统上的对抗性样本
7. PIndroid: A novel Android malware detection system using ensemble learning methods [O] . Idrees, F., Rajarajan, M., Conti, M., 2017

机译：PIndroid：使用集成学习方法的新型Android恶意软件检测系统

PIndroid: A novel Android malware detection system using ensemble learning methods

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