Advertisement

The Journal of Supercomputing

, Volume 72, Issue 8, pp 2991–3005 | Cite as

ELM-based spammer detection in social networks

  • Xianghan Zheng
  • Xueying Zhang
  • Yuanlong YuEmail author
  • Tahar Kechadi
  • Chunming Rong
Article

Abstract

Online social networks, such as Facebook, Twitter, and Weibo have played an important role in people’s common life. Most existing social network platforms, however, face the challenges of dealing with undesirable users and their malicious spam activities that disseminate content, malware, viruses, etc. to the legitimate users of the service. The spreading of spam degrades user experience and also negatively impacts server-side functions such as data mining, user behavior analysis, and resource recommendation. In this paper, an extreme learning machine (ELM)-based supervised machine is proposed for effective spammer detection. The work first constructs the labeled dataset through crawling Sina Weibo data and manually classifying corresponding users into spammer and non-spammer categories. A set of features is then extracted from message content and user behavior and applies them to the ELM-based spammer classification algorithm. The experiment and evaluation show that the proposed solution provides excellent performance with a true positive rate of spammers and non-spammers reaching 99 and 99.95 %, respectively. As the results suggest, the proposed solution could achieve better reliability and feasibility compared with existing SVM-based approaches.

Keywords

Social network Spammer Machine learning Extreme learning machine 

Notes

Acknowledgments

This paper is supported by the National Natural Science Foundation of China under Grant No. 61103175 and No.11271002, the Key Project of Chinese Ministry of Education under Grant No.212086; the Technology Innovation Platform Project of Fujian Province under Grant No. 2009J1007, No. 2013H6011 and 2013J01228; the Key Project Development Foundation of Education Committee of Fujian province under Grand No. JA11011 and JA12016.

References

  1. 1.
  2. 2.
    Bhat SY, Abulaish M (2013) Community-based features for identifying spammers in online social networks. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, pp 100–107Google Scholar
  3. 3.
    Grier C, Thomas K, Paxson V et al (2010) At spam: the underground on 140 characters or less[C]. In: Proceedings of the 17th ACM conference on computer and communications security. ACM, pp 27–37Google Scholar
  4. 4.
  5. 5.
    Liu Y, Wu B, Wang B et al (2014) SDHM: a hybrid model for spammer detection in Weibo. Advances in Social networks analysis and mining (ASONAM), 2014 IEEE/ACM international conference on. IEEE, pp 942–947Google Scholar
  6. 6.
    Rong HJ, Ong YS, Tan AH et al (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(1):359–366CrossRefGoogle Scholar
  7. 7.
    Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
  8. 8.
    Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Networks 2004. In: Proceedings 2004 IEEE international joint conference on. IEEE, vol 2, pp 985–990Google Scholar
  9. 9.
    Hirose Y, Yamashita K, Hijiya S (1991) Back-propagation algorithm which varies the number of hidden units. Neural Netw 4(1):61–66CrossRefGoogle Scholar
  10. 10.
    Shen H, Li Z (2014) Leveraging social networks for effective spam filtering. IEEE Trans Comput 11:2743–2759MathSciNetCrossRefGoogle Scholar
  11. 11.
    Uemura M, Tabata T (2008) Design and evaluation of a Bayesian-filter-based image spam filtering method, international conference on information security and assurance (ISA), IEEE, pp 46–51Google Scholar
  12. 12.
    Zhou B, Yao Y, Luo J (2013) Cost-sensitive three-way email spam filtering. J Intell Inf Syst 42(1):19–45CrossRefGoogle Scholar
  13. 13.
    Jung J, Sit E (2004) An empirical study of spam traffic and the use of DNS black Lists. In: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM, pp 370–375Google Scholar
  14. 14.
    Antonakakis M, Perdisci R, Dagon D, Lee W, Feamster N (2010) Building a dynamic reputation system for DNS, In: Proceedings of the third USENIX workshop on large-scale exploits and emergent threats (LEET)Google Scholar
  15. 15.
    Xu L, Zheng X, Rong C (2013) Trust evaluation based content filtering in social interactive data. In: Cloud computing and big data (CloudCom-Asia), 2013 international conference on. IEEE, pp 538–542Google Scholar
  16. 16.
    Kincaid J (2010) EdgeRank: the secret sauce that makes Facebook’s news feed tick. TechCrunchGoogle Scholar
  17. 17.
    Wang AH (2010) Don’t follow me: Spam detection in twitter. Security and cryptography (SECRYPT), Proceedings of the 2010 international conference on. IEEE, pp 1–10Google Scholar
  18. 18.
    Yardi S, Romero D, Schoenebeck G (2009) Detecting spam in a twitter network. First Monday 15(1)Google Scholar
  19. 19.
    Stringhini G, Kruegel C, Vigna G (2010) Detecting spammers on social networks. In: Proceedings of the 26th annual computer security applications conference. ACM, pp 1–9Google Scholar
  20. 20.
    Gao H, Chen Y, Lee K et al (2012) Towards online spam filtering in social networks, NDSSGoogle Scholar
  21. 21.
    Benevenuto F, Magno G, Rodrigues T et al (2010) Detecting spammers on twitter. Collab, Elect Messag Anti Abuse Spam Conf (CEAS), 6:12Google Scholar
  22. 22.
    Zheng X, Zeng Z, Chen Z et al (2015) Detecting spammers on social networks. Neurocomputing 159:27–34CrossRefGoogle Scholar
  23. 23.
    Lee K, Caverlee J, Webb S (2010) The social honeypot project: protecting online communities from spammers. In: Proceedings of the 19th international conference on World wide web. ACM, pp 1139–1140Google Scholar
  24. 24.
    Zhou Y, Chen K, Song L et al (2012) Feature analysis of spammers in social networks with active honeypots: a case study of Chinese microblogging networks. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012). IEEE Computer Society, pp 728–729Google Scholar
  25. 25.
    Miller Z, Dickinson B, Deitrick W et al (2014) Twitter spammer detection using data stream clustering. Inf Sci 260:64–73CrossRefGoogle Scholar
  26. 26.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRefGoogle Scholar
  27. 27.
    Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, New YorkzbMATHGoogle Scholar
  28. 28.
    Ghanty P, Paul S, Pal NR (2009) NEUROSVM: an architecture to reduce the effect of the choice of kernel on the performance of SVM. J Mach Learn Res 10:591–622Google Scholar
  29. 29.
    Huang GB, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1):155–163CrossRefGoogle Scholar
  30. 30.
    Zheng XH, Chen N, Chen Z et al (2014) Mobile cloud based framework for remote-resident multimedia discovery and access. J Intern Technol 15(6):1043–1050Google Scholar
  31. 31.
    Hinton GE (2007) Learning multiple layers of representation. Trends Cogn Sci 11(10):428–434CrossRefGoogle Scholar
  32. 32.
    Bengio Y (2014) Scaling up deep learning. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, p 1966.1Google Scholar
  33. 33.
    Zhou S, Chen Q, Wang X (2013) Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120:536–546CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Xianghan Zheng
    • 1
    • 2
  • Xueying Zhang
    • 1
    • 2
  • Yuanlong Yu
    • 1
    • 2
    Email author
  • Tahar Kechadi
    • 3
  • Chunming Rong
    • 4
  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  2. 2.Fujian Key Laboratory of Network Computing and Intelligent Information ProcessingFuzhouChina
  3. 3.School of Computer Science and InformaticsUniversity College DublinBelfieldIreland
  4. 4.Department of Electrical Engineering and Computer ScienceUniversity of StavangerStavangerNorway

Personalised recommendations