International Journal of Research and Development in Engineering Sciences (IJRDES ) | Deep Learning based Detection of Fraud in Online Recruitment

Deep Learning based Detection of Fraud in Online Recruitment

Concerned citizens might be reassured that this research examines a deep learning method for identifying fraud in online recruitment. Traditional systems for spotting rising fraud trends predominantly depend on rule-based screening, rendering them susceptible. In comparison to alternative methods, deep learning models, especially CNNs and RNNs, exhibit superior performance in identifying bogus job advertisements. The research utilizes a database of real and fabricated job adverts to derive pertinent textual and metadata for categorization objectives. A hybrid deep learning model integrates an attention mechanism with LSTM techniques to improve detection accuracy. The experimental findings indicate that the proposed model surpasses existing machine learning techniques in accuracy and reliability. The model's stability and generalizability are assessed through multiple datasets. Researchers may explore explainable AI systems for fraud detection in the future. This research significantly aids in the development of dependable online job boards.

Research Type: Policy Research
Paper Type: Analytical Research Paper
Vol.7 , Issue 4 , Pages: 5 – 10, Jul 2025
Published on: 09 Jul, 2025
Issue Type: Regular

Cite Score

:

100
No. of authors

:

75
No. of Downloads

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43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

About Authors:

K.S.ASIF MOHIDDIN

India

Viswam Engineering College

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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: K.S.ASIF MOHIDDIN, asifmohiddin28@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.