Using Weighted Based Feature Selection Technique for Android Malware Detection

Author:

Mazlan Nurul Hidayah,Hamid Isredza Rahmi A.

Publisher

Springer Singapore

Reference15 articles.

1. Abdullah Z, Saudi MM, Anuar NB (2014) Mobile botnet detection: Proof of concept. In: 2014 IEEE 5th control and system graduate research colloquium, pp 257–262

2. Kemp S (2016) We Are Social, “Digital in 2016”. www.wearesocial.com , http://wearesocial.com/sg/special-reports/digital-2016 . Accessed 05 Nov 2016

3. Abawajy J, Kelarev A (2017) Iterative classifier fusion system for the detection of android malware. IEEE Trans Big Data PP(99):1

4. Vidas T, Votipka D, Christin N (2011) All your droid are belong to us: a survey of current android attacks. In: WOOT 2011, pp 81–90

5. Data G (2016) G Data Releases Mobile Malware Report for the Fourth Quarter of 2015. https://www.gdata-software.com/g-data/newsroom/news/article/g-data-releases-mobile-malware-report-for-the-fourth-quarter-of-2015 . Accessed 05 Nov 2016. (Search Date 19/4/2016)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Android Malware Detection Using Machine Learning Techniques;2022 International Conference on Computational Science and Computational Intelligence (CSCI);2022-12

2. Efficient and Effective Static Android Malware Detection Using Machine Learning;Information Systems Security;2022

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