Abstract
Recently, the popularity of mobile devices has risen drastically due to the increased functionality of the devices. This matter forces a large number of security challenges that need high consideration. Android malware detection method can be divided into two types, which are static and dynamic analysis. Static techniques are often prone to high false negative rates due to evolution in code basis and code repacking, although fast and efficient. While dynamic and behavior based analysis aims to provide methods for effectively and efficiently extracting unique patterns of each malware family based on its behavior. To address some of those shortcomings, the study uses permission-based Android malware feature as a basis for malware detection using weighted based technique.
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Acknowledgement
The authors express appreciation to the Universiti Tun Hussein Onn Malaysia (UTHM). This research is supported by Postgraduate Research Grant vot number U610, Short Term Grant vot number U653 and Gates IT Solution Sdn. Bhd. under its publication scheme.
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Mazlan, N.H., Hamid, I.R.A. (2018). Using Weighted Based Feature Selection Technique for Android Malware Detection. In: Kim, K., Joukov, N. (eds) Mobile and Wireless Technologies 2017. ICMWT 2017. Lecture Notes in Electrical Engineering, vol 425. Springer, Singapore. https://doi.org/10.1007/978-981-10-5281-1_7
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DOI: https://doi.org/10.1007/978-981-10-5281-1_7
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