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A trust based model for recommendations of malignant people in social network

Author

Listed:
  • Govind Kumar Jha

    (Government Engineering College)

  • Hardeo Kumar Thakur

    (Bennett University)

  • Preetish Ranjan

    (Amity University)

  • Manish Gaur

    (Institute of Engineering and Technology, Dr. APJ Abdul Kalam Technical University)

Abstract

Interactions are often viewed in the context of social and mental relations, but the reality cannot be captured accurately by measuring the stochastic of its dynamics. This paper demonstrates an operational framework to detect socio-technical attacks through contextual analysis of the social network. It emphasized on a correlation based on the centrality that can be measured through Karl Pearson, Jaccard and Katz, etc. Given this insight, hidden or suspicious nodes cannot be identified through above mentioned approaches. This framework provides guidelines for modeling a network into a layered set of interacting nodes with dense intra-connections and sparse inter-connections. We proposed a methodology to filter out a pool of hidden users operating covertly within the network. In this work, result has been validated by traversing the real time, most devastating 26/11 Mumbai attack terrorist network and recommends the malignant people against the ground truth of social network.

Suggested Citation

  • Govind Kumar Jha & Hardeo Kumar Thakur & Preetish Ranjan & Manish Gaur, 2023. "A trust based model for recommendations of malignant people in social network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 415-428, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01812-0
    DOI: 10.1007/s13198-022-01812-0
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    References listed on IDEAS

    as
    1. Sharad Goel & Ashton Anderson & Jake Hofman & Duncan J. Watts, 2016. "The Structural Virality of Online Diffusion," Management Science, INFORMS, vol. 62(1), pages 180-196, January.
    2. Preetish Ranjan & Abhishek Vaish, 2021. "Socio-Technical Attack Approximation Based on Structural Virality of Information in Social Networks," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 15(1), pages 153-172, January.
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