[论文解读] AI-based Identity Fraud Detection: A Systematic Review

C Zhang, Asif Qumer Gill+2 more|ArXiv.org|Jan 16, 2025

Imbalanced Data Classification Techniques被引用 3

一句话总结

这篇论文对基于 AI 的身份欺诈检测方法进行了系统文献综述，提出了分类法并分析了来自 43 项研究（2020–2024）的开放挑战与趋势。

ABSTRACT

With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.

研究动机与目标

• 定义基于 AI 的身份欺诈检测与预防方法的总体格局。
• 开发将认证与持续认证方法区分的分类法。
• 识别该领域的开放技术与非技术挑战与趋势。

提出的方法

• 按预定义协议执行系统文献综述。
• 检索四个数据库（ACM、IEEE Xplore、ScienceDirect、Scopus）以获取 2020–2024 年刊物。
• 应用纳入/排除标准和六项质量评估标准来筛选研究。
• 使用结构化表单提取数据并将发现汇总为分类法与分析。

实验结果

研究问题

• RQ1 RQ: 关于基于 AI 的身份欺诈方法已有何知识？
• RQ2 RQ1: 当前基于 AI 的身份欺诈检测方法有哪些？
• RQ3 RQ2: 解决身份欺诈的关键开放挑战是什么？

主要发现

• 识别出三大基于 AI 的身份欺诈检测方法：生物识别、视觉异常检测和用户行为异常检测。
• 建立两阶段分类法：认证与持续认证。
• 发现深度学习主导视觉异常检测与用户行为异常检测；面部识别是讨论最多的生物识别方法。
• 报道的开放挑战：数据质量与多样性、数据隐私与安全、动态欺诈模式，以及训练/效率优化。
• 更多方法聚焦于认证（55.81%）而非持续认证（44.91%）；在认证任务中生物识别认证占主导（79% 的认证文章）。
• 持续认证依赖 UEBA，结合画像与异常检测技术。）