Linear SVM‐Based Android Malware Detection for Reliable IoT Services

Hyo-Sik Ham, Hwan-Hee Kim, Myung-Sup Kim and Mi-Jung Choi

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

Abstract: 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.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2014/594501

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:594501

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().