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Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern

by Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 52
Year of Publication: 2019
Authors: Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber
10.5120/ijca2019919369

Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber . Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern. International Journal of Computer Applications. 178, 52 ( Sep 2019), 19-30. DOI=10.5120/ijca2019919369

@article{ 10.5120/ijca2019919369,
author = { Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber },
title = { Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2019 },
volume = { 178 },
number = { 52 },
month = { Sep },
year = { 2019 },
issn = { 0975-8887 },
pages = { 19-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number52/30907-2019919369/ },
doi = { 10.5120/ijca2019919369 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:53:49.692517+05:30
%A Eman Osman
%A Mahmoud Mostafa
%A Sayed Abdel Gaber
%T Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 52
%P 19-30
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Online Social Networks (OSN) have a great role in increasing the communication among people. Their role never stops as they have become the way to share information and the real-time news. However, their unprecedented success has also attracted the attention of hackers, who use OSN to spread spam and malicious contents. Hackers have found a good environment, which is compatible with their goals in terms of widespread reach to the largest number of victims or even spreading large propaganda in a very short time. All this can be done using OSN. The presence of spam and malicious contents on OSN may lead to people’s aversion from these sites. This research tackles this phenomenon by introducing Flexible Malicious Accounts Detector (FMAD) solution, which can detect malicious and spam accounts using predefined features. Additionally, FMDA can identify newly emerging features and classify them as either normal or abnormal. Moreover, FMDA can recognize malicious accounts campaigns. Therefore, the presented solution performs better than all previous approaches that cannot deal with new emerging features. For this purpose, FMAD uses both supervised and unsupervised machine learning techniques. The experiment shows that FMAD results in accuracy reaching 99.75 %.

References
  1. S.S. Aksenova, WEKA Explorer Tutorial, Calif. State Univ. Sacramento. 11 (2006) 1–37. doi:10.1007/s10115-007-0114-2.
  2. A. Almaatouq, E. Shmueli, M. Nouh, A. Alabdulkareem, V.K. Singh, M. Alsaleh, A. Alarifi, A. Alfaris, A. Sandy, If it looks like a spammer and behaves like a spammer , it must be a spammer : analysis and detection of microblogging spam accounts, Int. J. Inf. Secur. (2016). doi:10.1007/s10207-016-0321-5.
  3. A.A. Amleshwaram, N. Reddy, S. Yadav, G. Gu, C. Yang, CATS: Characterizing automation of Twitter spammers, 2013 5th Int. Conf. Commun. Syst. Networks, COMSNETS 2013. (2013). doi:10.1109/COMSNETS.2013.6465541.
  4. F. Benevenuto, G. Magno, T. Rodrigues, V. Almeida, Detecting spammers on twitter, Collab. Electron. Messag. Anti-Abuse Spam Conf. 6 (2010) 12. doi:10.1.1.297.5340.
  5. N. Chavoshi, H. Hamooni, A. Mueen, DeBot: Twitter bot detection via warped correlation, Proc. - IEEE Int. Conf. Data Mining, ICDM. (2017) 817–822. doi:10.1109/ICDM.2016.86.
  6. Z. Chu, S. Gianvecchio, H. Wang, S. Jajodia, Who is Tweeting on Twitter: Human, Bot, or Cyborg?, ACSAC. (2010) 21. doi:10.1145/1920261.1920265.
  7. M. Egele, G. Stringhini, C. Kruegel, G. Vigna, COMPA: Detecting Compromised Accounts on Social Networks, Symp. Netw. Distrib. Syst. Secur. . (2013). http://www.people.vcu.edu/~cfung/bib/compromised_accounts_detection-ndss13.pdf (accessed April 4, 2018).
  8. A. Goyal, R. Mehta, Performance comparison of Naïve Bayes and J48 classification algorithms, Int. J. Appl. Eng. Res. 7 (2012) 1389–1393.
  9. N. Gupta, A. Aggarwal, P. Kumaraguru, Bit.ly/malicious: Deep dive into short URL based e-crime detection, eCrime Res. Summit, eCrime. 2014–Janua (2014) 14–24. doi:10.1109/ECRIME.2014.6963161.
  10. J. Han, J. Pei, Y. Yin, Mining Frequent P atterns without Candidate Generation, in: Conf. Manag. Data (SIGMOD’00, Dallas, TX), New York, NY, USA, 2000. https://www.cs.sfu.ca/~jpei/publications/sigmod00.pdf (accessed April 6, 2018).
  11. P.N. Howard, B. Kollanyi, Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum, (n.d.). http://ssrn.com/abstract=2798311 (accessed April 4, 2018).
  12. B. HSSINA, A. MERBOUHA, H. EZZIKOURI, M. ERRITALI, A comparative study of decision tree ID3 and C4.5, Int. J. Adv. Comput. Sci. Appl. 4 (2014) 13–19. doi:10.14569/SpecialIssue.2014.040203.
  13. W. Hua, Y. Zhang, Threshold and associative based classification for social spam profile detection on twitter, Proc. - 2013 9th Int. Conf. Semant. Knowl. Grids, SKG 2013. (2013) 113–120. doi:10.1109/SKG.2013.15.
  14. S. Huang, P. Adviser-Rastgoufard, A comparative study of clustering and classification algorithms, (2007) 170–178.
  15. S.D. Jadhav, H.P. Channe, Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques, Int. J. Sci. Res. 14611 (2013) 2319–7064. www.ijsr.net.
  16. P. Kakkar, A. Parashar, Comparison of Different Clustering Algorithms using WEKA Tool, Int. J. Adv. Res. Technol. Eng. Sci. 1 (2014) 20–22.
  17. H. Kaur, H. Kaur, Classification of data using New Enhanced Decision Tree Algorithm ( NEDTA ), Int. J. Emerg. Technol. Comput. Appl. Sci. ( IJETCAS ). (2014) 147–152.
  18. J. Leskovec, Stanford Large Network Dataset Collection, (n.d.). https://snap.stanford.edu/data/ (accessed April 5, 2018).
  19. Y. Liu, Random forest algorithm in big data environment, Comput. Model. NEW Technol. 18 (2014) 147–151.
  20. Kaya Mehmet, S. Conley, A. Varol, Visualization of the Social Bot’s Fingerprints, 4th Int. Symp. Digit. Forensics Secur. IEEE. (2016) 161–166.
  21. Z. Miller, B. Dickinson, W. Deitrick, W. Hu, A.H. Wang, Twitter spammer detection using data stream clustering, Inf. Sci. (Ny). 260 (2014) 64–73. doi:10.1016/j.ins.2013.11.016.
  22. K. Mumtaz, M. Studies, T. Nadu, An Analysis on Density Based Clustering of Multi Dimensional Spatial Data, Indian J. Comput. Sci. Eng. 1 (2010) 8–12.
  23. S. Shehnepoor, M. Salehi, R. Farahbakhsh, N. Crespi, NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media, IEEE Trans. Inf. Forensics Secur. 12 (2017) 1585–1595. doi:10.1109/TIFS.2017.2675361.
  24. A. Singh, S. Batra, Ensemble based spam detection in social IoT using probabilistic data structures, Futur. Gener. Comput. Syst. 81 (2018) 359–371. doi:10.1016/j.future.2017.09.072.
  25. G. Stringhini, C. Kruegel, G. Vigna, Detecting Spammers on Social Networks, ACSAC. (2010) 1–9. http://www.cse.fau.edu/~xqzhu/courses/Resources/GSC.acsac10-socialnets.pdf.
  26. C. Teljstedt, M. Rosell, F. Johansson, A Semi-automatic Approach for Labeling Large Amounts of Automated and Non-automated Social Media User Accounts, Proc. - 2nd Eur. Netw. Intell. Conf. ENIC 2015. (2015) 155–159. doi:10.1109/ENIC.2015.31.
  27. D. Trang, F. Johansson, M. Rosell, Evaluating Algorithms for Detection of Compromised Social Media User Accounts, Proc. - 2nd Eur. Netw. Intell. Conf. ENIC 2015. (2015) 75–82. doi:10.1109/ENIC.2015.19.
  28. I. Tudor, Association Rule Mining as a Data Mining Technique, Univ. Pet. Din Ploiesti. LX (2008) 49–56.
  29. B. Umale, Overview of K-means and Expectation Maximization Algorithm for Document Clustering, (2014) 5–8.
  30. V.N. Vapnik, Statistical Learning Theory, Adapt. Learn. Syst. Signal Process. Commun. Control. 2 (1998) 1–740. doi:10.2307/1271368.
  31. R. Venkatesh, J.K. Rout, S.K. Jena, Malicious account detection based on short URLs in twitter, Master Technol. Natl. Inst. Technol. Rourkela. 395 (2015) 243–251. doi:10.1007/978-81-322-3592-7_24.
  32. M.J. Zaki, S. Parthasarathy, M. Ogihara, W. Li, New Algorithms for Fast Discovery of Association Rules, 3rd Intl Conf Knowl. Discov. Data Min. 20 (1997) 283–286. doi:10.1.1.42.5143.
  33. X. Zhang, S. Zhu, W. Liang, Detecting spam and promoting campaigns in the Twitter social network, Proc. - IEEE Int. Conf. Data Mining, ICDM. (2012) 1194–1199. doi:10.1109/ICDM.2012.28.
  34. X. Zheng, Z. Zeng, Z. Chen, Y. Yu, C. Rong, Detecting spammers on social networks, Neurocomputing. 159 (2015) 27–34. doi:10.1016/j.neucom.2015.02.047.
  35. Last.fm dataset 360K - MTG - Music Technology Group (UPF), (2010). https://www.upf.edu/web/mtg/lastfm360k (accessed April 5, 2018).
  36. Datasets, (2017). https://botometer.iuni.iu.edu/bot-repository/datasets.html.
  37. Tweets2011, Tweets2011, (2011).
  38. Google Transparency Report, Google Transparency Report, (2010). https://transparencyreport.google.com/.
  39. Spamhaus, https://www.spamhaus.org/lookup/.
Index Terms

Computer Science
Information Sciences

Keywords

OSN Spam Malicious account detection datamining Association rules.

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