Machine Learning Algorithms for Spam Detetction in Social Networks

  • Authors

    • Yenuga Padma
    • Dr. Y.K.Sundara Krishna
    https://doi.org/10.14419/ijet.v7i3.24.22809
  • Machine learning, social networks, spam detection, WEKA and Rapid miner.

  • Most of the web based social systems like Face book, twitter, other mailing systems and social networks are developed for users to share their information, to interact and engage with the community. Most of the times these social networks will give some troubles to the users by spam messages, threaten messages, hackers and so on.. Many of the researchers worked on this and gave several approaches to detect the spam, hackers and other trouble shoots. In this paper we are discussing some tools to detect the spam messages in social networks. Here we are using RF, SVM, KNN and MLP machine learning algorithms across rapid miner and WEKA. It gives the better results when compared with other tools.

     

     

  • References

    1. [1] Uncovering social spammers: social honey pots and machine learning by kyumin lee, James caver lee and Steve webb, https://dl.acm.org/citation.cfm?id=1835522

      [2] Proposed efficient algorithm to filter spam using machine learning techniques by Ali Shafiguhaski, Navid k Sourati Pacific Science Review A: Natural Science and Engineering Volume 18, Issue 2, July 2016, Pages 145-149

      [3] Performance Evaluation of Machine Learning Algorithms for Spam Profile Detection on Twitter Using WEKA and RapidMiner by Hanif, MohamadHazimMd; Adewole, KayodeSakariyah; Anuar, Nor Badrul; Kamsin, Amirrudin Source: Advanced Science Letters, Volume 24, Number 2, February 2018, pp. 1043-1046(4)

      [4] Detecting Spam with Azure Machine Learning by Scott Clayton, 12 Feb 2018.

      [5] SMS spam detection and comparison of various machine learning algorithms by Paras sethi et.al https://ieeexplore.ieee.org/document/8284445/

      [6] Spam campaign detection, analysis, and investigation by son dinh et.al https://www.sciencedirect.com/science/article/pii/S1742287615000079

      [7] SAAD, content based Web Spam Analyzer and Detector by Victor.M. Prieto et.al https://www.sciencedirect.com/science/article/pii/S0164121213001684.

      [8] An automated frame work for document spam detection using enhanced context feature matching by Y.Padma et.al in www.ijarcs.info in volume number 9 number 1 jan-feb 2018.

      [9] Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010). @spam: the underground on 140 characters or less. Proceedings of the 17th ACM conference on Computer and communications security, 27-37.

      [10] Chu, Z., Gianvecchio, S., Wang, H., &Jajodia, S. (2012). Detecting automation of twitter accounts: Are you a human, bot, or cyborg? IEEE Transactions on Dependable and Secure Computing, 9(6), 811-824. doi:10.1109/TDSC.2012.75

      [11] Narudin, F. A., Feizollah, A., Anuar, N. B., &Gani, A. (2014). Evaluation of machine learning classifiers for mobile malware detection. Soft Computing, 1-15.

      [12] Smola, A. J., &Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.

      [13] Noriega, L. (2005). Multilayer perceptron tutorial. School of Computing. Staffordshire University

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  • How to Cite

    Padma, Y., & Y.K.Sundara Krishna, D. (2018). Machine Learning Algorithms for Spam Detetction in Social Networks. International Journal of Engineering & Technology, 7(3.24), 536-540. https://doi.org/10.14419/ijet.v7i3.24.22809