Oases: An Online Scalable Spam Detection System for Social Networks

Hailu Xu, Liting Hu, Pinchao Liu, Yao Xiao, Wentao Wang, Jai Dayal, Qingyang Wang, Yuzhe Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Cloud Computing, CLOUD 2018 - Part of the 2018 IEEE World Congress on Services
PublisherIEEE Computer Society
Pages98-105
Number of pages8
ISBN (Electronic)9781538672358
DOIs
StatePublished - Sep 7 2018
Event11th IEEE International Conference on Cloud Computing, CLOUD 2018 - San Francisco, United States
Duration: Jul 2 2018Jul 7 2018

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
Volume2018-July
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Other

Other11th IEEE International Conference on Cloud Computing, CLOUD 2018
CountryUnited States
CitySan Francisco
Period7/2/187/7/18

Keywords

  • DHT based overlay
  • Online social networks
  • Spam detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software

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  • Cite this

    Xu, H., Hu, L., Liu, P., Xiao, Y., Wang, W., Dayal, J., Wang, Q., & Tang, Y. (2018). Oases: An Online Scalable Spam Detection System for Social Networks. In Proceedings - 2018 IEEE International Conference on Cloud Computing, CLOUD 2018 - Part of the 2018 IEEE World Congress on Services (pp. 98-105). [8457788] (IEEE International Conference on Cloud Computing, CLOUD; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.1109/CLOUD.2018.00020