Vol. 4 No. 2 (2025): April
RESEARCH ARTICLES

Investigation of Deep Learning Approaches for Online Recruitment Fraud Detection

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  • G Mabuchan,
  • B Rizwana,
  • K Vinod Kumar,
  • A Jasmitha,
  • P Prasanthi
G Mabuchan
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
B Rizwana
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Vinod Kumar
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
A Jasmitha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Prasanthi
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15163735

Published 2025-04-06

Keywords

  • Class imbalance,
  • data augmentation,
  • deep learning,
  • employment scam,
  • fraud detection,
  • online recruitment,
  • SMOTE
    • ...More
    Less

How to Cite

G Mabuchan, B Rizwana, K Vinod Kumar, A Jasmitha, & P Prasanthi. (2025). Investigation of Deep Learning Approaches for Online Recruitment Fraud Detection. International Journal of Computational Learning & Intelligence, 4(2), 420–431. https://doi.org/10.5281/zenodo.15163735

Copyright (c) 2025 G Mabuchan, B Rizwana, K Vinod Kumar, A Jasmitha, P Prasanthi

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Modern companies increasingly rely on digital platforms to recruit new employees and streamline the hiring process. However, the surge in online job postings has led to a rise in fraudulent advertisements, where scammers exploit job seekers for financial gain. Online recruitment fraud has become a significant concern in cybercrime, necessitating effective detection mechanisms to combat fake job listings. While traditional machine learning and deep learning models have been employed for this task, this research explores the effectiveness of two transformer-based deep learning models—Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimized BERT Pretraining Approach (RoBERTa)  in accurately identifying fraudulent job postings.
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