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

Govind Kumar Jha (), Hardeo Kumar Thakur (), Preetish Ranjan () and Manish Gaur ()
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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

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 1, No 36, 415-428

Abstract: 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.

Keywords: Recommendations; Socio-technical attacks; Social network analysis; Trust; machine learning; Correlation coefficients; SVD (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13198-022-01812-0

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