Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91605
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: 2021
Source: International journal of intelligent systems, 2021, Early View, https://doi.org/10.1002/int.22625
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
Appears in Collections:Journal/Magazine Article

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