用弱监督森林与加密认证，精准识别社交传感器云的身份克隆。
Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication
- 基于弱监督深度森林分析用户非敏感特征，识别相似身份
- 在真实数据集上检测准确率显著优于现有方法
- 适合安全审计与云服务提供商反克隆场景
近年来，社交传感器云服务中的身份克隆事件呈上升趋势。然而，现有方法存在性能不佳、缺乏重复账号检测方案、且缺少在真实数据集上的大规模评估等问题。本文提出一种新型身份克隆检测方法，包含两个核心组件：1）相似身份检测方法；2）基于密码学的认证协议。首先，我们构建了一个弱监督深度森林模型，利用服务提供方提供的非隐私敏感用户特征，识别相似身份。随后，设计了一种基于密码学的认证协议，验证这些相似身份是否由同一服务商生成。在大规模真实数据集上的实验表明，该方法在可行性与性能上均优于当前最先进的身份克隆检测技术。
原文摘要 · Abstract (English)
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.
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