Linear SVM-Based Android Malware Detection and Feature Selection for Performance Improvement 


Vol. 39,  No. 8, pp. 738-745, Aug.  2014


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  Abstract

Recently, mobile users continuously increase, and mobile applications also increase As mobile applications increase, the mobile users used to store sensitive and private information such as Bank information, location information, ID, password on their mobile devices. Therefore, recent malicious application targeted to mobile device instead of PC environment is increasing. In particular, since the Android is an open platform and includes security vulnerabilities, attackers prefer this environment. This paper analyzes the performance of malware detection system applying linear SVM machine learning classifier to detect Android malware application. This paper also performs feature selection in order to improve detection performance.

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  Cite this article

[IEEE Style]

K. Kim and M. Choi, "Linear SVM-Based Android Malware Detection and Feature Selection for Performance Improvement," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 8, pp. 738-745, 2014. DOI: .

[ACM Style]

Ki-Hyun Kim and Mi-Jung Choi. 2014. Linear SVM-Based Android Malware Detection and Feature Selection for Performance Improvement. The Journal of Korean Institute of Communications and Information Sciences, 39, 8, (2014), 738-745. DOI: .

[KICS Style]

Ki-Hyun Kim and Mi-Jung Choi, "Linear SVM-Based Android Malware Detection and Feature Selection for Performance Improvement," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 8, pp. 738-745, 8. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880