一种在线招聘欺诈检测的智能模型

Bandar Alghamdi, Fahad M. Alharby
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引用次数: 32

摘要

这项研究试图通过创建一个可靠的模型来防止个人和组织的隐私和金钱损失,该模型可以检测在线招聘环境中的欺诈暴露。本研究的主要贡献在于使用基于随机森林分类器的集成方法来检测在线招聘欺诈(ORF)的可靠检测模型。网络招聘欺诈检测的特点是其他类型的电子欺诈检测以其现代性和稀缺性对这一概念进行研究。为了达到本研究的目的,研究人员提出了检测模型。特征选择采用支持向量机方法,分类检测采用随机森林集成分类器。一个名为就业骗局爱琴海数据集(EMSCAD)的免费数据集用于应用该模型。在选择和分类采用之前,已经应用了预处理步骤。结果显示,获得的准确率为97.41%。此外,研究结果提出了检测目的的主要特征和重要因素,包括具有公司简介特征、公司标志特征和行业特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Model for Online Recruitment Fraud Detection
This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.
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