Linear SVM-Based Android Malware Detection for Reliable IoT Services
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ham, Hyo-Sik | - |
dc.contributor.author | Kim, Hwan-Hee | - |
dc.contributor.author | Kim, Myung-Sup | - |
dc.contributor.author | Choi, Mi-Jung | - |
dc.date.accessioned | 2021-12-28T22:40:17Z | - |
dc.date.available | 2021-12-28T22:40:17Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 1110-757X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/133533 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | HINDAWI LTD | - |
dc.title | Linear SVM-Based Android Malware Detection for Reliable IoT Services | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Myung-Sup | - |
dc.identifier.doi | 10.1155/2014/594501 | - |
dc.identifier.scopusid | 2-s2.0-84937010475 | - |
dc.identifier.wosid | 000343508700001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF APPLIED MATHEMATICS | - |
dc.relation.isPartOf | JOURNAL OF APPLIED MATHEMATICS | - |
dc.citation.title | JOURNAL OF APPLIED MATHEMATICS | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.