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基于数量级的链路分析在虚假身份检测中的应用
Order-of-Magnitude Based Link Analysis for False Identity Detection |
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| 课程网址: |
http://videolectures.net/qr09_shen_omb/
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| 主讲教师: |
Tossapon Boongoen, Qiang Shen
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| 开课单位: |
阿伯里斯特威斯大学
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| 开课时间: |
2009-07-22
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| 课程语种: |
英语
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| 中文简介: |
打击身份欺诈是至关重要和紧迫的，因为虚假身份已成为所有严重犯罪的共同标准，包括黑手党贩运和恐怖主义。检测虚假身份使用的典型方法依赖于文本和其他基于内容的特征的相似性度量，这些特征通常不适用于欺骗性和错误描述的情况。可以通过在通信行为，金融交互和社交网络中呈现的链接信息来克服该障碍。基于定量链接的相似性度量已被证明可有效识别因特网和出版物领域中的类似问题。然而，这些数值方法仅集中于链接结构，并且未能实现对信息的准确和相干解释。受此观察的启发，本文提出了一种新的定性相似性度量，它利用多个链接属性来重新定义基础相似性估计过程，从而产生语义丰富的相似性描述符。该方法基于数量级推理。它的适用性和性能通过实验评估与恐怖主义相关的数据集，并进一步推广出版数据。
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| 课程简介: |
Combating identity fraud is crucial and urgent as false identity has become the common denominator of all serious crime, including mafia trafficking and terrorism. Typical approaches to detecting the use of false identity rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of deceptive and erroneous description. This barrier can be overcome through link information presented in communication behaviors, financial interactions and social networks. Quantitative link-based similarity measures have proven effective for identifying similar problems in the Internet and publication domains. However, these numerical methods only concentrate on link structures, and fail to achieve accurate and coherent interpretation of the information. Inspired by this observation, this paper presents a novel qualitative similarity measure that makes use of multiple link properties to refine the underlying similarity estimation process and consequently derive semantic-rich similarity descriptors. The approach is based on order-of-magnitude reasoning. Its applicability and performance are experimentally evaluated over a terrorism-related dataset, and further generalized with publication data.
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| 关 键 词: |
犯罪; 虚假身份; 链接结构
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-09-14：lxf
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| 阅读次数: |
77
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