Abstract—Over the last years, with the tremendous growth of digital transformation and the constant need for companies to hire employees, huge amounts of fraudulent jobs have been posted on the internet. A cleverly planned sort of scam aimed at job searchers for a variety of unprofessional purposes is a false job posting. It can lead to a loss of money and effort. An Arabic intelligent model has been built to avoid fraudulent jobs on the Internet using machine learning, data mining, and classification techniques. The proposed model is applied to the Arabic version of the EMSCAD dataset. It is available on the Internet in the English version and it has been retrieved from the use of a real-life system and consists of several features such as company profile, company logo, interview questions, and more features depending on job offer ads, Firstly, EMSCAD is translated into the Arabic language. Then, a set of different classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) was used to detect the fraudulent jobs. Finally, the results were compared to determine the best classifier used for detecting fraudulent jobs. The proposed model achieved better results when using a Random Forest classifier with 97% accuracy.

Keywords—data mining, fraud detection, online recruitment, machine learning, EMSCAD dataset

Cite: Mohamed A. Sofy, Mohammed H. Khafagy, and Rasha M. Badry, "An Intelligent Arabic Model for Recruitment Fraud Detection Using Machine Learning," Journal of Advances in Information Technology, Vol. 14, No. 1, pp. 102-111, February 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.