Classification of concurrent anomalies for iot software based support vector machine
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wu, Zhiqiang | - |
dc.contributor.author | Abbas, Asad | - |
dc.contributor.author | Chen, Xin | - |
dc.contributor.author | Lee, Scott Uk-Jin | - |
dc.date.accessioned | 2021-06-22T13:02:49Z | - |
dc.date.available | 2021-06-22T13:02:49Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2018-02 | - |
dc.identifier.issn | 1992-8645 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/7935 | - |
dc.description.abstract | Internet of Thing (IoT) can connect anyone with anything at any point in any place. Currently, growing number of IoT devices have become a major role of daily life owing to their convenience. The IoT devices usually controlled by Web applications and mobile applications, which will process lots of events from user’s controller to devices. Hence, such software is a kind of concurrent program in IoT environment because the software is unable to simultaneously process these events, which may cause the concurrent issue. There is event-drive model in either Web application or mobile applications, which is unable to easily detect the concurrent anomaly by existing approaches due to the non-determined of execution and hardly reproduced by the same sequence. The previous techniques of concurrent detection are excessive limitations that only used for one of concurrent anomaly with the large number of false positive. In this paper, we describe a novel methodology to dynamically classify two types of concurrent anomalies for IoT software. According to the executable sequence graph, we generate the training and test examples for classification. The vectorization features are classified by Support Vector Machine (SVM) with Gaussian kernel. The SVM will predict the concurrent state of current executable example. As a result, the optimal true positive of simulation is 80% in our experiment which is a higher accuracy than others. © 2005 – ongoing JATIT & LLS. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Little Lion Scientific | - |
dc.title | Classification of concurrent anomalies for iot software based support vector machine | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Scott Uk-Jin | - |
dc.identifier.scopusid | 2-s2.0-85042389977 | - |
dc.identifier.bibliographicCitation | Journal of Theoretical and Applied Information Technology, v.96, no.3, pp.832 - 842 | - |
dc.relation.isPartOf | Journal of Theoretical and Applied Information Technology | - |
dc.citation.title | Journal of Theoretical and Applied Information Technology | - |
dc.citation.volume | 96 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 832 | - |
dc.citation.endPage | 842 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Classification | - |
dc.subject.keywordAuthor | Concurrency anomalies | - |
dc.subject.keywordAuthor | IoT software | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Support vector machine | - |
dc.identifier.url | https://www.researchgate.net/publication/323279886_Classification_of_concurrent_anomalies_for_iot_software_based_support_vector_machine | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.