Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{205040,
        author = {Mohammed Faiz and A. D. Wakhare},
        title = {RecruitGuard: A Review of Deep Learning Techniques for Fraudulent Job Posting Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4823-4828},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205040},
        abstract = {The rapid growth of online recruitment platforms has increased the risk of fake job postings and recruitment fraud. Fraudulent job advertisements can mislead job seekers, compromise personal information, and damage organizational credibility. Traditional detection methods are often inefficient due to the large volume and dynamic nature of online job data. This review paper analyzes existing fake job detection techniques based on Machine Learning, Deep Learning, Natural Language Processing, and hybrid models. It highlights the advantages and limitations of approaches such as LSTM, Bi-LSTM, and ensemble learning. The study also identifies key research gaps and emphasizes the need for accurate, explainable, and real-time fake job detection systems.},
        keywords = {Fake Jobs Detection, Online Recruitment Fraud, LSTM, NLP, Machine Learning, Deep Learning, Bi-LSTM, Fraud Detection, Real-Time Verification},
        month = {June},
        }