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组合技术用于检测在线社交网络中的个人资料克隆攻击

Irfan Mohiuddin¹, Ahmad Almogren¹

¹Chair of Cyber Security, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

PeerJ. Computer science

|September 24, 2025

概括

由于人工智能生成的内容,检测虚假的在线个人资料至关重要. 这项研究引入了一个强大的框架来识别LinkedIn上的克隆和人工智能生成的个人资料,达到96%以上的准确性.

科学领域:

计算机科学计算机科学
人工智能的人工智能
社交网络分析社交网络分析

背景情况:

在线社交网络 (OSN) 面临着越来越多的身份欺骗挑战.
人工智能生成的内容和大型语言模型 (LLM) 复杂化了传统的检测方法.
检测复杂的假个人资料,包括克隆和人工智能生成的,至关重要.

研究的目的:

引入一个新的基于内容的多阶段框架,用于检测LinkedIn上的个人资料克隆.
将个人资料分为四类:合法,人类克隆,LLM生成的合法和LLM生成的克隆.
解决传统方法对抗人工智能驱动的假冒的不足.

主要方法:

通过基于注意的部分嵌入聚合学习语义表示.
语言风格建模使用风度-复杂性特征.
通过基于集群的异常值检测和通过外折叠堆叠集成分类进行异常评分.

主要成果:

拟议的元组模型显著优于基线方法.
在3600个配置文件的数据集上,实现了宏观平均准确性,精度,回忆和F1分数超过96%的准确性和精度.
在检测人造和人工智能生成的假冒中表现出有效性.

结论:

介绍了一种强大且可扩展的以内容为驱动的方法,用于在OSN中检测身份欺骗.
结合语义,风格和概率信号,可以有效地检测各种形式的个人资料冒充.
该框架成功地解决了在身份欺骗中人工智能生成的内容的挑战.

关键词:

异常检测检测异常检测基于注意力的聚合.组合学习学习组合学习一个元集分类器.在线社交网络在线社交网络困惑度评分困惑度评分个人资料克隆检测检测语义嵌入式语义嵌入式造型仪分析分析造型仪分析

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