{"ID":2882727,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09665","arxiv_id":"2508.09665","title":"Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication","abstract":"Recent years have witnessed a rising trend in social-sensor cloud identity cloning incidents. However, existing approaches suffer from unsatisfactory performance, a lack of solutions for detecting duplicated accounts, and a lack of large-scale evaluations on real-world datasets. We introduce a novel method for detecting identity cloning in social-sensor cloud service providers. Our proposed technique consists of two primary components: 1) a similar identity detection method and 2) a cryptography-based authentication protocol. Initially, we developed a weakly supervised deep forest model to identify similar identities using non-privacy-sensitive user profile features provided by the service. Subsequently, we designed a cryptography-based authentication protocol to verify whether similar identities were generated by the same provider. Our extensive experiments on a large real-world dataset demonstrate the feasibility and superior performance of our technique compared to current state-of-the-art identity clone detection methods.","short_abstract":"Recent years have witnessed a rising trend in social-sensor cloud identity cloning incidents. However, existing approaches suffer from unsatisfactory performance, a lack of solutions for detecting duplicated accounts, and a lack of large-scale evaluations on real-world datasets. We introduce a novel method for detectin...","url_abs":"https://arxiv.org/abs/2508.09665","url_pdf":"https://arxiv.org/pdf/2508.09665v1","authors":"[\"Ahmed Alharbi\",\"Hai Dong\",\"Xun Yi\"]","published":"2025-08-13T09:53:23Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.LG\",\"cs.SI\"]","methods":"[]","has_code":false}