Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91605
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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.creatorLi, WJen_US
dc.creatorKe, LSen_US
dc.creatorMeng, WZen_US
dc.creatorHan, JGen_US
dc.date.accessioned2021-11-17T08:51:18Z-
dc.date.available2021-11-17T08:51:18Z-
dc.identifier.issn0884-8173en_US
dc.identifier.urihttp://hdl.handle.net/10397/91605-
dc.language.isoenen_US
dc.publisherJohn Wiley & Sonsen_US
dc.rights© 2021 Wiley Periodicals LLCen_US
dc.rightsThis is the peer reviewed version of the following article: Li, W, Ke, L, Meng, W, Han, J. An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors. Int J Intell Syst. 2022; 37: 287- 304, which has been published in final form at https://doi.org/https://doi.org/10.1002/int.22625. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.en_US
dc.subjectArtificial intelligenceen_US
dc.subjectEmail classificationen_US
dc.subjectIoT securityen_US
dc.subjectSpam detectionen_US
dc.subjectSupervised learningen_US
dc.titleAn empirical study of supervised email classification in Internet of Things : practical performance and key influencing factorsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage287en_US
dc.identifier.epage304en_US
dc.identifier.volume37en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1002/int.22625en_US
dcterms.abstractInternet of Things (IoT) is gradually adopted by many organizations to facilitate the information collection and sharing. In an organization, an IoT node usually can receive and send an email for event notification and reminder. However, unwanted and malicious emails are a big security challenge to IoT systems. For example, attackers may intrude a network by sending emails with phishing links. To mitigate this issue, email classification is an important solution with the aim of distinguishing legitimate and spam emails. Artificial intelligence especially machine learning is a major tool for helping detect malicious emails, but the performance might be fluctuant according to specific datasets. The previous research figured out that supervised learning could be acceptable in practice, and that practical evaluation and users' feedback are important. Motivated by these observations, we conduct an empirical study to validate the performance of common learning algorithms under three different environments for email classification. With over 900 users, our study results validate prior observations and indicate that LibSVM and SMO-SVM can achieve better performance than other selected algorithms.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of intelligent systems, Jan. 2022, v. 37, no. 1, p. 287-304en_US
dcterms.isPartOfInternational journal of intelligent systemsen_US
dcterms.issued2022-01-
dc.identifier.isiWOS:000686361700001-
dc.description.validate202111 bcwhen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera1068-n05-
dc.identifier.SubFormID43872-4-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China (No. 61802077)en_US
dc.description.pubStatusPublisheden_US
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