Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication

TL;DR

This paper presents a novel approach combining a weakly supervised deep forest model and cryptographic authentication to detect social-sensor identity cloning, achieving superior performance on large real-world datasets.

Contribution

It introduces a new hybrid method for identity cloning detection that leverages machine learning and cryptography, addressing limitations of existing approaches.

Findings

Outperforms current state-of-the-art methods in detection accuracy

Effective on large-scale real-world social-sensor datasets

Combines machine learning with cryptographic verification for robust detection

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.