Have a personal or library account? Click to login
Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks Cover

Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks

Foundations of Computing and Decision Sciences
Volume 40 (2015): Issue 2 (June 2015)
By: Muhammad Abulaish and  Sajid Y. Bhat  
Open Access
|May 2015

References

  1. [1] Bhat S. Y., Abulaish M., Community-based features for identifying spammers in online social networks, in: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), ACM, 2013, 100-107.10.1145/2492517.2492567
    Search in Google Scholar
  2. [2] Bhat S. Y., Abulaish M., Analysis and mining of online social networks: emerging trends and challenges, WIREs: Data Mining and Knowledge Discovery, 3, 6, 2013, 408-444.10.1002/widm.1105
    Search in Google Scholar
  3. [3] Bhat S. Y., Abulaish M., Mirza A. A., Spammer classification using ensemble methods over structural social network features, in: Proceedings of the 14th IEEE/WIC/ACM International Conference on Web Intelligence (WI), Warsaw, Poland, 2014, 454-458.10.1109/WI-IAT.2014.133
    Search in Google Scholar
  4. [4] Bhat S. Y., Abulaish M., HOCTracker: Tracking the evolution of hierarchical and overlapping communities in dynamic social networks, IEEE Transactions on Knowledge and Data Engineering, 27, 4, 2014, 1019-1032.10.1109/TKDE.2014.2349918
    Search in Google Scholar
  5. [5] Bilge L., Strufe T., Balzarotti D., Kirda E., All your contacts are belong to us: automated identity theft attacks on social networks, in: Proceedings of the 18th International Conference on World Wide Web (WWW), ACM, NY, USA, 2009, 551-560.10.1145/1526709.1526784
    Search in Google Scholar
  6. [6] Bouguessa M., An unsupervised approach for identifying spammers in social networks, in: Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, Washington DC, USA, 2011, 832-840.10.1109/ICTAI.2011.130
    Search in Google Scholar
  7. [7] Breiman L., Bagging predictors, Machine Learning, 24, 2, 1996, 123-140.10.1007/BF00058655
    Search in Google Scholar
  8. [8] Carpinter J. M., Evaluating Ensemble Classifiers for Spam Filtering, Honours Thesis, University of Canterbury, 2005.
    Search in Google Scholar
  9. [9] Caruana R., Niculescu-Mizil A., Crew G., Ksikes A., Ensemble selection from libraries of models, in: Proceedings of the 21st International Conference on Machine Learning, 2004, 137–144.10.1145/1015330.1015432
    Search in Google Scholar
  10. [10] Dietterich T. G., Ensemble methods in machine learning, Lecture Notes in Computer Science, 1857, 2000, 1–15.10.1007/3-540-45014-9_1
    Search in Google Scholar
  11. [11] Douceur J. R., The sybil attack, in: Revised Papers from the 1st International Workshop on Peer-to-Peer Systems, Springer-Verlag, London, UK, 2002, 251-260.10.1007/3-540-45748-8_24
    Search in Google Scholar
  12. [12] Erdélyi M., Garzó A., Benczúr A. A., Web spam classification: a few features worth more, in: Proceedings of the Joint WICOW/AIRWeb Workshop on Web Quality, ACM, 2011, 27-34.10.1145/1964114.1964121
    Search in Google Scholar
  13. [13] Fire M., Katz G., Elovici Y., Strangers intrusion detection-detecting spammers and fake proles in social networks based on topology anomalies, Human Journal, 1, 1, 2012, 26–39.
    Search in Google Scholar
  14. [14] Frank E., Hall M., Holmes G., Kirkby R., Pfahringer B., Witten I., Trigg L. Weka, O. Maimon and L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook, Springer, 2005, 1305-1314.10.1007/0-387-25465-X_62
    Search in Google Scholar
  15. [15] Freund Y., Schapire R. E., Experiments with a new boosting algorithm, in Proceedings of the 13th International Conference on Machine Learning, 1996, 325-332.
    Search in Google Scholar
  16. [16] Gan Q., Suel, T., Improving web spam classifiers using link structure, in: Proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), ACM, NY, USA 2007, 17–20.10.1145/1244408.1244412
    Search in Google Scholar
  17. [17] Gao H., Hu J., Wilson C., Li Z., Chen Y., Zhao B. Y., Detecting and characterizing social spam campaigns, in: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (IMC), ACM, NY, USA, 2010, 35–47.10.1145/1879141.1879147
    Search in Google Scholar
  18. [18] Geng G. G., Wang C. H., Li Q. D., Xu L., Jin X. B., Boosting the performance of web spam detection with ensemble under-sampling classification, in: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'07), IEEE, 2007, 583-587.10.1109/FSKD.2007.207
    Search in Google Scholar
  19. [19] Gomes L. H., Almeida R. B., Bettencourt L. M. A., Almeida V., Almeida J. M., Comparative graph theoretical characterization of networks of spam and legitimate email, in: Proceedings of the 2nd Conference on Email and Anti-Spam (CEAS), 2005, 1-8.
    Search in Google Scholar
  20. [20] Jiang J., Wilson C., Wang X., Sha W., Huang P., Dai Y., Zhao B. Y., Understanding latent interactions in online social networks, ACM Transactions on the Web, 7, 4, 2013.10.1145/2517040
    Search in Google Scholar
  21. [21] John G. H., Langley P., Estimating continuous distributions in bayesian classifiers, in: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI), San Francisco, USA, 1995, 338-345.
    Search in Google Scholar
  22. [22] Kiran P., Atmosukarto I., Spam or Not Spam – that is the question, Technical Report, University of Washington, URL: http://www.cs.washington.edu/homes/indria/research/spamfilterraviindri.pdf, Date of access: Apr 1, 2014.
    Search in Google Scholar
  23. [23] Krombholz K., Hobel H., Huber M., Weippl E., Advanced social engineering attacks, Journal of Information Security and Applications, 2014, 1-10.
    Search in Google Scholar
  24. [24] Lam Ho-Y., Yeung Dit-Y., A Learning approach to spam detection based on social networks, in: Proceedings of the 4th Conference on Email and Anti-Spam (CEAS), Mountain View, California, 2007.
    Search in Google Scholar
  25. [25] Lancichinetti A., Fortunato S., Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities, Physical Review E, 80, 2009.10.1103/PhysRevE.80.01611819658785
    Search in Google Scholar
  26. [26] Mislove A., Marcon M., Gummadi K. P., Druschel P., Bhattacharjee B., Measurement and analysis of online social networks, in: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, ACM, 2007, 29-42.10.1145/1298306.1298311
    Search in Google Scholar
  27. [27] Neumayer R., Clustering based ensemble classification for spam filtering, in: Proceedings of the 7th Workshop on Data Analysis (WDA'06), 2006, 11-22.
    Search in Google Scholar
  28. [28] Quinlan J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, USA, 1993.
    Search in Google Scholar
  29. [29] Ramachandran A., Feamster N., Vempala S., Filtering spam with behavioral blacklisting, in: Proceedings of the 14th ACM Conference on Computer and Communications Security (CCS), ACM, NY, USA, 2007, 342-351.10.1145/1315245.1315288
    Search in Google Scholar
  30. [30] Shrivastava N., Majumder A., Rastogi R., Mining (social) network graphs to detect random link attacks, in: Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE), IEEE, Washington DC, 2008, 486-495.10.1109/ICDE.2008.4497457
    Search in Google Scholar
  31. [31] Stringhini G., Kruegel C., Vigna G., Detecting spammers on social networks, in: Proceedings of the 26th Annual Computer Security Applications Conference (ACSAC), ACM, NY, USA, ACM, 2010, 1–9.10.1145/1920261.1920263
    Search in Google Scholar
  32. [32] Viswanath B., Mislove A., Cha M., Gummadi K. P., On the evolution of user interaction in Facebook, in: Proceedings of the Workshop on Online Social Networks, 2009, 37-42.10.1145/1592665.1592675
    Search in Google Scholar
  33. [33] Xie Y., Yu F., Achan K., Panigrahy R., Hulten G., Osipkov I., Spamming botnets: signatures and characteristics, SIGCOMM Computing Communication Review, 38, 4, 2008, 171-182.10.1145/1402946.1402979
    Search in Google Scholar
DOI: https://doi.org/10.1515/fcds-2015-0006 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Journal RSS Feed
Language: English
Page range: 89 - 105
Submitted on: Dec 7, 2014
Accepted on: Mar 30, 2015
Published on: May 16, 2015
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Social network security,
Spammer detection,
Ensemble learning,
Classifier ensembles,
Feature extraction
Related subjects:
Computer sciences,
Artificial intelligence,
Software development

© 2015 Muhammad Abulaish, Sajid Y. Bhat, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 40 (2015): Issue 2 (June 2015)Next article