Abstract :

Online Recruitment Fraud (ORF) Has Emerged As A Significant Cybersecurity Threat With The Rapid Expansion Of Online Job Portals And Digital Hiring Platforms. Fraudulent Job Postings Deceive Job Seekers By Offering Fake Employment Opportunities, Often Leading To Financial Loss, Identity Theft, And Data Misuse. Traditional Detection Methods Based On Manual Verification And Rule-based Filtering Systems Are Insufficient Due To The Evolving Nature Of Fraudulent Strategies And The Large Volume Of Online Job Advertisements. Therefore, An Intelligent And Automated Detection System Is Required To Effectively Identify Fraudulent Recruitment Posts In Real Time. This Project Proposes A Deep Learning-based Online Recruitment Fraud (ORF) Detection System That Leverages Natural Language Processing (NLP) Techniques To Analyze Textual Job Descriptions And Related Metadata. The System Preprocesses Job Postings Through Tokenization, Stop-word Removal, Normalization, And Feature Extraction Before Feeding Them Into Deep Learning Models Such As LSTM, CNN, Or Transformer-based Architectures. These Models Learn Complex Linguistic Patterns, Contextual Relationships, And Semantic Inconsistencies That Commonly Appear In Fraudulent Job Advertisements. The System Classifies Job Posts As Legitimate Or Fraudulent While Generating Confidence Scores To Support Decision-making. Experimental Evaluation Demonstrates Improved Accuracy, Precision, Recall, And F1- Score Compared To Traditional Machine Learning Approaches. The Proposed Framework Enhances Fraud Detection Capability, Reduces False Positives, And Supports Scalable Deployment For Online Job Portals. By Integrating Deep Learning With Intelligent Text Analysis, The System Contributes To Safer Online Recruitment Environments And Protects Job Seekers From Digital Employment Scams.

Published:

02-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

62-69

Section:

Articles

License:

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

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