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Social Spammer Detection Based on PSO-CatBoost

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12382))

Abstract

With the rapid development of social networks, more and more organizations or individuals use social media to communicate with each other, passing on information and getting information, etc. However, while bringing convenience to people, social media has also become the main target of malicious attackers who try to take advantage of the system vulnerability and cause harm to other normal users, they obtain benefits mainly through sending false information, advertising links, phishing, etc. In this paper, firstly, we collect the features of spammers from the four views (profile, behavior, relationship, and interaction) for a more comprehensive analysis of spammers, secondly, we creatively combine the features of Particle Swarm Optimization (PSO) and CatBoost algorithm, and finally, we propose a novel PSO-CatBoost model based on the CatBoost model for detecting spammers. In order to validate the effectiveness of our proposed model, some ensemble learning algorithms are compared, and the experimental results show that our model outperforms other models.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61762018), the Guangxi 100 Youth Talent Program (F-KA16016) and the Colleges and Universities Key Laboratory of Intelligent Integrated Automation, Guilin University of Electronic Technology, China (GXZDSY2016-03),the research funding of Guangxi Key Lab of Multi-source Information Mining & Security (18-A-02-02), Natural Science Foundation of Guangxi (2018GXNSFAA281310), the Guangxi Key Research and Development Funding (2019AB35004). This work was supported in part by the Innovation special project of Zhongshan Science and Technology Bureau under Grant 2019AG001.

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Authors and Affiliations

  1. College of Electronics and Engineering, Guangxi Normal University, Guilin, 541004, China

    Shupeng Li, F. Jiang & Yunbai Qin

  2. Guangdong Aohong Technology Co., LTD., Zhongshan, 528437, China

    Kunkun Zheng

Authors
  1. Shupeng Li
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  2. F. Jiang
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  3. Yunbai Qin
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  4. Kunkun Zheng
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Corresponding authors

Correspondence to F. Jiang or Yunbai Qin .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Aeronautics and Astronautics, Nanjing University, Nanjing, China

    Bing Chen

  3. Computer Science, Georgia State University, Atlanta, GA, USA

    Wei Li

  4. College of Science and Engineering, Qatar Foundation Education City, Doha, Qatar

    Roberto Di Pietro

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Xuefeng Yan

  6. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Hao Han

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Li, S., Jiang, F., Qin, Y., Zheng, K. (2021). Social Spammer Detection Based on PSO-CatBoost. In: Wang, G., Chen, B., Li, W., Di Pietro, R., Yan, X., Han, H. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2020. Lecture Notes in Computer Science(), vol 12382. Springer, Cham. https://doi.org/10.1007/978-3-030-68851-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-68851-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68850-9

  • Online ISBN: 978-3-030-68851-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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