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

[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, 100-107.10.1145/2492517.2492567Search in Google Scholar

[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.1105Search in Google Scholar

[3] Bhat S. Y., Abulaish M., Mirza A. A., Spammer classification using ensemble methods over structural social network features, in: Proceedings of the 14^th IEEE/WIC/ACM International Conference on Web Intelligence (WI), Warsaw, Poland, 2014, 454-458.10.1109/WI-IAT.2014.133Search in Google Scholar

[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.2014.2349918Search in Google Scholar

[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 18^th International Conference on World Wide Web (WWW), ACM, NY, USA, 2009, 551-560.10.1145/1526709.1526784Search in Google Scholar

[6] Bouguessa M., An unsupervised approach for identifying spammers in social networks, in: Proceedings of the IEEE 23^rd International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, Washington DC, USA, 2011, 832-840.10.1109/ICTAI.2011.130Search in Google Scholar

[7] Breiman L., Bagging predictors, Machine Learning, 24, 2, 1996, 123-140.10.1007/BF00058655Search in Google Scholar

[8] Carpinter J. M., Evaluating Ensemble Classifiers for Spam Filtering, Honours Thesis, University of Canterbury, 2005.Search in Google Scholar

[9] Caruana R., Niculescu-Mizil A., Crew G., Ksikes A., Ensemble selection from libraries of models, in: Proceedings of the 21^st International Conference on Machine Learning, 2004, 137–144.10.1145/1015330.1015432Search in Google Scholar

[10] Dietterich T. G., Ensemble methods in machine learning, Lecture Notes in Computer Science, 1857, 2000, 1–15.10.1007/3-540-45014-9_1Search in Google Scholar

[11] Douceur J. R., The sybil attack, in: Revised Papers from the 1^st International Workshop on Peer-to-Peer Systems, Springer-Verlag, London, UK, 2002, 251-260.10.1007/3-540-45748-8_24Search in Google Scholar

[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, 27-34.10.1145/1964114.1964121Search in Google Scholar

[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] 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_62Search in Google Scholar

[15] Freund Y., Schapire R. E., Experiments with a new boosting algorithm, in Proceedings of the 13^th International Conference on Machine Learning, 1996, 325-332.Search in Google Scholar

[16] Gan Q., Suel, T., Improving web spam classifiers using link structure, in: Proceedings of the 3^rd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), ACM, NY, USA 2007, 17–20.10.1145/1244408.1244412Search in Google Scholar

[17] Gao H., Hu J., Wilson C., Li Z., Chen Y., Zhao B. Y., Detecting and characterizing social spam campaigns, in: Proceedings of the 10^th ACM SIGCOMM Conference on Internet Measurement (IMC), ACM, NY, USA, 2010, 35–47.10.1145/1879141.1879147Search in Google Scholar

[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 4^th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'07), IEEE, 2007, 583-587.10.1109/FSKD.2007.207Search in Google Scholar

[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 2^nd Conference on Email and Anti-Spam (CEAS), 2005, 1-8.Search in Google Scholar

[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, 2013.10.1145/2517040Search in Google Scholar

[21] John G. H., Langley P., Estimating continuous distributions in bayesian classifiers, in: Proceedings of the 11^th Conference on Uncertainty in Artificial Intelligence (UAI), San Francisco, USA, 1995, 338-345.Search in Google Scholar

[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] 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] Lam Ho-Y., Yeung Dit-Y., A Learning approach to spam detection based on social networks, in: Proceedings of the 4^th Conference on Email and Anti-Spam (CEAS), Mountain View, California, 2007.Search in Google Scholar

[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.80.01611819658785Search in Google Scholar

[26] Mislove A., Marcon M., Gummadi K. P., Druschel P., Bhattacharjee B., Measurement and analysis of online social networks, in: Proceedings of the 7^th ACM SIGCOMM Conference on Internet Measurement, ACM, 2007, 29-42.10.1145/1298306.1298311Search in Google Scholar

[27] Neumayer R., Clustering based ensemble classification for spam filtering, in: Proceedings of the 7^th Workshop on Data Analysis (WDA'06), 2006, 11-22.Search in Google Scholar

[28] Quinlan J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, USA, 1993.Search in Google Scholar

[29] Ramachandran A., Feamster N., Vempala S., Filtering spam with behavioral blacklisting, in: Proceedings of the 14^th ACM Conference on Computer and Communications Security (CCS), ACM, NY, USA, 2007, 342-351.10.1145/1315245.1315288Search in Google Scholar

[30] Shrivastava N., Majumder A., Rastogi R., Mining (social) network graphs to detect random link attacks, in: Proceedings of the IEEE 24^th International Conference on Data Engineering (ICDE), IEEE, Washington DC, 2008, 486-495.10.1109/ICDE.2008.4497457Search in Google Scholar

[31] Stringhini G., Kruegel C., Vigna G., Detecting spammers on social networks, in: Proceedings of the 26^th Annual Computer Security Applications Conference (ACSAC), ACM, NY, USA, ACM, 2010, 1–9.10.1145/1920261.1920263Search in Google Scholar

[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, 37-42.10.1145/1592665.1592675Search in Google Scholar

[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, 171-182.10.1145/1402946.1402979Search in Google Scholar