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A novel trust prediction approach for online social networks based on multifaceted feature similarity

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Abstract

Online Social Networks (OSNs) have gained popularity in recent years. Millions of people use Facebook, Instagram, Twitter, and LinkedIn. Malicious users can target users using security weaknesses like cloning and Sybil attacks and join their friend list or trusted network. Malicious people can send unwanted friend requests to other users. Before communicating with dubious users, users should know their trust level. In addition, existing social networks do not provide any system to assess the trustworthiness of people that make friend requests. Also, several existing trust models assume that participants’ direct trust ties are known and only focus on particular characteristics. Hence, a holistic model is required to measure explicit trust and infer indirect trust between participants. Using comprehensive feature similarity, we offer a unique OSN trust prediction technique. We choose features based on user interactions, relationships, preferences, behaviours, and activities. The retrieved features are utilised to measure direct and indirect trust between neighbours and non-neighbours. To compare the proposed trust prediction approach to existing approaches, we implement cloning and Sybil attack detection as exemplary applications. The empirical findings and comparisons with other methodologies verify the proposed approach’s effectiveness, efficiency, and superiority.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Parul Institute of Technology, FET, Parul University, Vadodara, Gujarat, 391760, India

    Gordhan Jethava

  2. Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, 395007, India

    Udai Pratap Rao

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  1. Gordhan Jethava
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  2. Udai Pratap Rao
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Correspondence to Udai Pratap Rao.

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Jethava, G., Rao, U.P. A novel trust prediction approach for online social networks based on multifaceted feature similarity. Cluster Comput 25, 3829–3843 (2022). https://doi.org/10.1007/s10586-022-03617-z

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  • DOI: https://doi.org/10.1007/s10586-022-03617-z

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