Predicting Online Job Recruitment Fraudulent Using Machine Learning

Ishrat Jahan Mouri, Biman Barua, M. Mesbahuddin Sarker, Alistair Barros, Md Whaiduzzaman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Employing individuals via the Internet has been a boon for businesses in the modern day. It is much simpler and more convenient than traditional recruitment methods. However, several scammers are abusing this platform, which may result in financial and privacy loss for job seekers and damage to the reputable organisation's name. In this research, we proposed a technique for detecting Online Recruitment Fraud (ORF). This model uses a publicly available dataset containing 17,780 job postings. We apply the four classification models to determine which classification model performs best for our suggested model. In this model, we use decision trees, random forests, Naive Bayes and logistic regression methods. We have estimated and evaluated the accuracy of several prediction systems. The random forest classifier provides the greatest accuracy, 97.16%, on our dataset. We have endeavoured to develop a method for detecting bogus recruiting postings.

Original languageEnglish
Title of host publicationProceedings of 4th International Conference on Communication, Computing and Electronics Systems - ICCCES 2022
EditorsV. Bindhu, João Manuel Tavares, Chandrasekar Vuppalapati
PublisherSpringer Science and Business Media Deutschland GmbH
Pages719-733
Number of pages15
ISBN (Print)9789811977527
DOIs
Publication statusPublished - 2023
Event4th International Conference on Communication, Computing and Electronics Systems, ICCCES 2022 - Coimbatore, India
Duration: 15 Sept 202216 Sept 2022

Publication series

NameLecture Notes in Electrical Engineering
Volume977
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference4th International Conference on Communication, Computing and Electronics Systems, ICCCES 2022
Country/TerritoryIndia
CityCoimbatore
Period15/09/2216/09/22

Keywords

  • Classification model
  • Machine learning
  • Natural language processing
  • Online recruitment fraud

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