We propose a novel method to detect identity cloning of social-sensor cloud service providers to prevent the detrimental outcomes caused by identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks and a powerful deep learning model to perform cloned identity detection. We evaluated the proposed method against the state-of-the-art identity cloning detection techniques and the other popular identity deception detection models atop a real-world dataset. The results show that our method significantly outperforms these techniques/models in terms of Precision and F1-score.
@article{arxiv.2110.10897,
title = {Privacy-Aware Identity Cloning Detection based on Deep Forest},
author = {Ahmed Alharbi and Hai Dong and Xun Yi and Prabath Abeysekara},
journal= {arXiv preprint arXiv:2110.10897},
year = {2021}
}
Comments
The 19th International Conference on Service Oriented Computing (ICSOC 2021). arXiv admin note: text overlap with arXiv:2109.15179