Fuz-Spam: Label Smoothing-Based Fuzzy Detection of Spammers in Internet of Things

Nowadays, online spamming has already been a remarkable threat to contents security of Internet of Things. Due to constant technical progress, online spamming activities have been more and more concealed. This brings much fuzziness to spammer detection scenarios, yielding the issue of fuzzy detectio...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 30; no. 11; pp. 4543 - 4554
Main Authors: Guo, Zhiwei, Yu, Keping, Jolfaei, Alireza, Ding, Feng, Zhang, Ning
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cyberspace
Deep learning
Feature extraction
Fuzzy detection
Internet of Things
Internet of Things (IoT)
label smoothing
Semantics
Smoothing
Smoothing methods
Social networking (online)
spammers
Spamming
Unsolicited e-mail
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:Nowadays, online spamming has already been a remarkable threat to contents security of Internet of Things. Due to constant technical progress, online spamming activities have been more and more concealed. This brings much fuzziness to spammer detection scenarios, yielding the issue of fuzzy detection of spammers. Although existing detection techniques for spammers utilized idea of deep learning, they still ignore to release power of label spaces. As real nature about a user may be usually fuzzy, but the label annotated for a user is always certain. To remedy such gap, this article proposes a label smoothing-based fuzzy detection method for spammers (Fuz-Spam). First of all, deep representation is still utilized to deeply fuse features, which acts as the foundation of neural computing. On this basis, generative adversarial learning is introduced to transform previous label spaces into distributed forms. In addition, two groups of experiments are carried out on two real-world datasets for evaluation. The results demonstrate that the Fuz-Spam improves identification efficiency about 10% to 20% than previous ones, and that the Fuz-Spam is endowed with proper stability.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2021.3130311