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Permission-based Feature Selection for Android Malware Detection and Analysis

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Chit La Pyae Myo Hein, Khin Mar Myo
10.5120/ijca2018917902

Chit La Pyae Myo Hein and Khin Mar Myo. Permission-based Feature Selection for Android Malware Detection and Analysis. International Journal of Computer Applications 181(19):29-39, September 2018. BibTeX

@article{10.5120/ijca2018917902,
	author = {Chit La Pyae Myo Hein and Khin Mar Myo},
	title = {Permission-based Feature Selection for Android Malware Detection and Analysis},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2018},
	volume = {181},
	number = {19},
	month = {Sep},
	year = {2018},
	issn = {0975-8887},
	pages = {29-39},
	numpages = {11},
	url = {http://www.ijcaonline.org/archives/volume181/number19/29974-2018917902},
	doi = {10.5120/ijca2018917902},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Malware is spreading around the world and infecting not only for ending users, but also for large organizations and service providers. There is a real need of a dimension reduction approach of malware features for better detection. This system describes for malware detection and characterization framework which is based on Static Approach by only analyzing the Manifest File of android application. This system also describes a Feature Selection Approach, which is also based on Manifest File Analysis for the purpose of dimension reducing of malware features. Firstly, a number of Permission-Based Features are extracted by disassembling the Manifest File of Android application. Then, feature dimensions are reduced by proposing Score-based Approach. The results getting from the Correlation and Information Gain are used to compare the results of Score-Based Features Selection. According to the experimental results, proposed a light-weight approach can perform as equal as other feature selection methods. After feature selection, manifest file analysis based on malware classification and characterization results are also described in this system. The classification results tested by without reducing features and the results obtained by reducing features are compared to determine which methods or classifiers are the best to detect malware.

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Keywords

Android Security, Malware, Smartphone.