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Home»Articles»Machine Learning Algorithms for Spam Detection in Social Networks

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Machine Learning Algorithms for Spam Detection in Social Networks

Author : K. Nagaramani, K. Vandanarao and B. Mamatha
Volume 8 No.3 Special Issue:June 2019 pp 41-44

Abstract

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.

Keywords

Machine Learning, Social Networks, Spam Detection, WEKA and Rapid Miner

Full Text:

References

[1] Kyumin lee, [Online] Available at:https://dl.acm.org/citation.cfm?id=1835522.
[2] Ali Shafiguhaski and k NavidSourati, “Proposed efficient algorithm to filter spam using machine learning techniques”Pacific Science Review A: Natural Science and Engineering, Vol.18, No. 2, pp. 145-149, July 2016.
[3] Hanif, MohamadHazimMd, Adewole, KayodeSakariyah,Anuar, Nor Badrul,Kamsin, and Amirrudin, “Performance Evaluation of Machine Learning Algorithms for Spam Profile Detection on Twitter Using WEKA and RapidMiner”, Advanced Science Letters, Vol. 24, No. 2, pp. 1043-1046, Feb. 2018.
[4] Scott Clayton, Detecting Spam with Azure Machine Learning, 12 Feb 2018.
[5] Parassethi, [Online] Available at: https://ieeexplore.ieee.org/document/8284445/
[6] Sondinh, [Online] Available at: https://www.sciencedirect.com/science/article/pii/S1742287615000079.
[7] M .VictorPrieto, [Online] Available at: https://www.sciencedirect.com/science/article/pii/S016412121300 1684.
[8] Y. Padma, “An automated frame work for document spam detection using enhanced context feature matching”, [Online] Available at:www.ijarcs.info, Vol. 9, No.1,Jan-Feb 2018.
[9] C. Grier, K. Thomas, V. Paxson, and M. Zhang, @spam: the underground on 140 characters or less”, Proceedings of the 17th ACM conference on Computer and communications security, pp. 27-37, 2010.
[10] Z. Chu, S. Gianvecchio, H. Wang and S. Jajodia, “Detecting automation of twitter accounts: Are you a human bot, or cyborg?”, IEEE Transactions on Dependable and Secure Computing, Vol. 9, No. 6, pp.811-824, 2012
[11] F.A. Narudin, A. Feizollah, N.B. Anuar and A. Gani, “Evaluation of machine learning classifiers for mobile malware detection”, Soft Computing, pp. 1-15, 2014.
[12] A.J. Smola and B. Schölkopf, “A tutorial on support vector regression”, Statistics and computing, pp. 199-222, 2004.
[13] L. Noriega, “Multilayer perceptron tutorial”, School of Computing, Staffordshire University, 2005.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

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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.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]uniandes.edu.co
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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