NPS-AntiClone: Identity Cloning Detection based on Non-Privacy-Sensitive User Profile Data

• URL: http://arxiv.org/abs/2109.15179v1
• Date: Thu, 30 Sep 2021 14:49:07 GMT
• Title: NPS-AntiClone: Identity Cloning Detection based on Non-Privacy-Sensitive User Profile Data
• Authors: Ahmed Alharbi, Hai Dong, Xun Yi and Prabath Abeysekara
• Abstract summary: Social sensing is a paradigm that allows crowdsourcing data from humans and devices. Attackers intrude social-sensor clouds by cloning SocSen service providers' user profiles. We propose a novel unsupervised SocSen service provider identity cloning detection approach, NPS-AntiClone.
• Score: 7.257277734039782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social sensing is a paradigm that allows crowdsourcing data from humans and devices. This sensed data (e.g. social network posts) can be hosted in social-sensor clouds (i.e. social networks) and delivered as social-sensor cloud services (SocSen services). These services can be identified by their providers' social network accounts. Attackers intrude social-sensor clouds by cloning SocSen service providers' user profiles to deceive social-sensor cloud users. We propose a novel unsupervised SocSen service provider identity cloning detection approach, NPS-AntiClone, to prevent the detrimental outcomes caused by such identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks to perform cloned identity detection. It consists of three main components: 1) a multi-view account representation model, 2) an embedding learning model and 3) a prediction model. The multi-view account representation model forms three different views for a given identity, namely a post view, a network view and a profile attribute view. The embedding learning model learns a single embedding from the generated multi-view representation using Weighted Generalized Canonical Correlation Analysis. Finally, NPS-AntiClone calculates the cosine similarity between two accounts' embedding to predict whether these two accounts contain a cloned account and its victim. We evaluated our proposed approach using a real-world dataset. The results showed that NPS-AntiClone significantly outperforms the existing state-of-the-art identity cloning detection techniques and machine learning approaches.

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