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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fwygz7e3z5d5rdz63lntpjwy6y" style="color: black;">Journal of Information Security</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/jis.2019.103009">doi:10.4236/jis.2019.103009</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mpbt554535dvhifjbuhpl4oyki">fatcat:mpbt554535dvhifjbuhpl4oyki</a> </span>
more »... s 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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