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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. ※   2013 ()        (2013R1A1A3011698) First Author : Dept. of Computer Science, Kangwon National University, kkh1258@kangwon.ac.kr,  ° Corresponding Author : Dept. of Computer Science, Kangwon National University, mjchoi@kangwon.ac.kr,  :KICS2014-05-216, Received May 31 2014; Revised July 10, 2014; Accepted July 10, 2014 I.      ,           . 2013 4       3,500       2009 11  47    74          /  SVM          Feature  739   .           ,  , , ,       .           PC        ,        .             .                .                    .                PC    .                 .               .          feature      ,     .                      .      iOS, , , ,   .         iOS.               .      ,                   .    ASEC    2013 1  132 6139    .   67 3599,  65 2540  .  1 2013      . F-Secure           2014 1(1~3)      99% .  1% iOS  .      ,           .              .       ,      SVM(Support Vector Machine)       .       ,    feature    feature       .  1. 2013      Fig. 1. Monthly receipt rate of mobile malware in 2013

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