A fusion fuzzy model for detecting phony accounts in social networks

  • Authors

    • P. Srinivas Rao Assoc.Professor
    • Jayadev Gyani Assistant Professor
    • G. Narsimha Professor
    2018-09-26
    https://doi.org/10.14419/ijet.v7i4.17345
  • Datasets, Fuzzy modeling, Machine Learning, Online Social Network, Phony Account,

  • Abstract

    In online social network’s phony account detection is one of the major task among the ability of genuine user from forged user account. The fundamental objective of detection of phony account framework is to detect fake account and removal technique in Social network user sites. This work concentrates on detection of phony account in which it depends on normal basis framework, transformative Algorithms and fuzzy technique. Initially, the most essential attributes including personal attributes, comparability techniques and various real user review, tweets, or comments are extricated. A direct blend of these attributes demonstrates the significance of each reviews tweets comments etc. To compute closeness measure, a consolidated strategy in view of artiï¬cial honey bee state Algorithm and fuzzy technique are utilized. Second approach is proposed to alter the best weights of the normal user attributes utilizing the social network activities/transaction and inherited Algorithm. Finally, a normal rank rationale framework is utilized to calculate the ï¬nal scoring of normal user activities. The decision making of proposed approach to find phony account are variation with existing techniques user behavioral analysis using data sets and machine learning techniques such as crowdflower_sample and genuine_accounts_sample dataset of facebook and Twitter. The outcomes demonstrate that proposed strategy overcomes the previously mentioned strategies.

     

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  • How to Cite

    Srinivas Rao, P., Gyani, J., & Narsimha, G. (2018). A fusion fuzzy model for detecting phony accounts in social networks. International Journal of Engineering & Technology, 7(4), 2738-2742. https://doi.org/10.14419/ijet.v7i4.17345

    Received date: 2018-08-11

    Accepted date: 2018-08-23

    Published date: 2018-09-26