Open Access
2014 Linear SVM-Based Android Malware Detection for Reliable IoT Services
Hyo-Sik Ham, Hwan-Hee Kim, Myung-Sup Kim, Mi-Jung Choi
J. Appl. Math. 2014(SI01): 1-10 (2014). DOI: 10.1155/2014/594501


Current many Internet of Things (IoT) services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.


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Hyo-Sik Ham. Hwan-Hee Kim. Myung-Sup Kim. Mi-Jung Choi. "Linear SVM-Based Android Malware Detection for Reliable IoT Services." J. Appl. Math. 2014 (SI01) 1 - 10, 2014.


Published: 2014
First available in Project Euclid: 27 February 2015

Digital Object Identifier: 10.1155/2014/594501

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI01 • 2014
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