University of Oulu

Xin Liu, Pingjun Zou, Weishan Zhang, et al., “CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing,” Wireless Communications and Mobile Computing, vol. 2017, Article ID 1457870, 9 pages, 2017. doi:10.1155/2017/1457870

CPSFS : a credible personalized spam filtering scheme by crowdsourcing

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Author: Liu, Xin1; Zou, Pingjun1; Zhang, Weishan1;
Organizations: 1College of Computer & Communication Engineering China University of Petroleum (East China), Qingdao, China
2University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Hindawi, 2017
Publish Date: 2018-02-15


Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

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Series: Wireless communications and mobile computing
ISSN: 1530-8669
ISSN-E: 1530-8677
ISSN-L: 1530-8669
Volume: 2017
Article number: 1457870
DOI: 10.1155/2017/1457870
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: The work presented in this paper is supported by the Key Research Program of Shandong Province (no. 2017GGX10140), National Natural Science Foundation of China (no. 61309024, and no. 61772551), the Program on Innovative Methods of Work from Ministry of Science and Technology, China (no. 2015010300), Shandong Provincial Natural Science Foundation (no. ZR2015FM022), and the Fundamental Research Funds for the Central Universities.
Copyright information: Copyright © 2017 Xin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.