Abstract :

The Rapid Growth Of Online Recruitment Platforms Has Created New Opportunities For Job Seekers, But It Has Also Led To A Significant Rise In Online Recruitment Fraud (ORF), Where Malicious Actors Post Fake Job Advertisements To Exploit Individuals Financially Or Steal Sensitive Information. Traditional Fraud Detection Methods, Such As Rule-based Filtering And Manual Verification, Are Often Insufficient To Handle The Scale, Diversity, And Evolving Nature Of Fraudulent Job Postings. This Paper Proposes An Advanced ORF Detection System Using Deep Learning Approaches To Automatically Identify And Classify Fraudulent Recruitment Content. The Proposed System Leverages Deep Learning Models Such As Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), And Transformer-based Architectures To Analyze Textual And Contextual Features Of Job Postings. It Incorporates Data Preprocessing Techniques Including Tokenization, Normalization, And Feature Embedding To Enhance Model Performance. The System Is Trained On Labeled Datasets Containing Both Genuine And Fraudulent Job Listings And Is Evaluated Using Metrics Such As Accuracy, Precision, Recall, And F1-score. Experimental Results Demonstrate That Deep Learning Models Significantly Outperform Traditional Machine Learning Approaches In Detecting Recruitment Fraud. The Proposed Approach Provides A Scalable, Efficient, And Robust Solution For Safeguarding Job Seekers And Improving Trust In Online Recruitment Platforms.

Published:

06-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1567-1572

Section:

Articles

License:

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

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