A Semantics and Behaviors-Collaboratively Driven Spammer Detection Method

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A Semantics and Behaviors-Collaboratively Driven Spammer Detection Method

Guo, Zhiwei / Yang, Jinhui / Yu, Keping

As online spamming has posed serious security threat to cyberspaces, relevant detection technologies based on artificial intelligence is being widely studied. Existing related research literatures can be divided into two classes: methods based on behavior patterns and methods based on semantic patterns. In order to better solve this challenge, we clearly proposed a semantics and behaviors-collaboratively driven spammer detection Method (Co-Sdm) in social networks. In particular, long-term behavior and semantic pattern of multi-source information fusion and collaborative coding is introduced. Therefore, a more comprehensive feature space representation can be captured to further detect spammers and improve the ability to deal with spam. In the experiment, we carried out a series of experiments under different scenarios and basic parameters based on two real datasets. Compare the high efficiency of Co-Sdm clearly proposed with the three baselines of multiple evaluation index values. The test results show that, compared with the baseline, the average characteristic of Co-Sdm has improved by about 5%.

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Document information

Title :

A Semantics and Behaviors-Collaboratively Driven Spammer Detection Method

Additional title:

Lect. Notes Electrical Eng.

Contributors:

Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Guo, Zhiwei (author) / Yang, Jinhui (author) / Yu, Keping (author)

Conference:

International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021

Published in:

Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Chapter : 132 ; 1320-1329

Lecture Notes in Electrical Engineering ; 861

Publication date :

2022-03-18

Size :

10 pages

ISBN :

978-981-16-9492-9 , 978-981-16-9491-2

ISSN :