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Title: An empirical study of supervised email classification in Internet of Things : practical performance and key influencing factors
Authors: Li, WJ 
Ke, LS
Meng, WZ
Han, JG
Issue Date: Jan-2022
Source: International journal of intelligent systems, Jan. 2022, v. 37, no. 1, p. 287-304
Abstract: Internet 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.
Keywords: Artificial intelligence
Email classification
IoT security
Spam detection
Supervised learning
Publisher: John Wiley & Sons
Journal: International journal of intelligent systems 
ISSN: 0884-8173
DOI: 10.1002/int.22625
Rights: © 2021 Wiley Periodicals LLC
This 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 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.
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