International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Machine Learning Model for detecting Fraudulent Job Listings on Recruitment Platforms

Abstract

The growing menace of fake job postings has become one of the main issues in the online job market, which wastes the time and resources of job seekers and may harm them. Many such fraudulent postings disguise themselves as real opportunities for job seekers to get confused. The research aims to solve the problem using machine learning algorithm like Random Forest (RF), Logistic Regression (LR) and neural networks, which can classify a job posting as either real or fake by extracting features and posting patterns. We strive to develop an machine learning (ML) model capable of detecting fake job postings effectively and preventing the gullible job seekers from potential scams. Advantages of the proposed system include improvement in efficiency and scalability, potential reduction in multiple platforms of phony job posts.

Country : India

1 B.Venkata Harivardhan Reddy2 P.S.Omprakash3 A.Karthikram

  1. Computer Science & Engineering (Cyber Security), Madanapalle institute of Technology & Science, Madanapalle-517 325, A.P., India
  2. Computer Science & Engineering (Cyber Security), Madanapalle institute of Technology & Science, Madanapalle-517 325, A.P., India
  3. Assistant Professor, Computer Science & Engineering (Cyber Security), Madanapalle institute of Technology & Science, Madanapalle-517 325, A.P., India

IRJIET, Volume 9, Special Issue of INSPIRE’25 April 2025 pp. 217-224

doi.org/10.47001/IRJIET/2025.INSPIRE35

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References

  1. N. R, B. R, R. Balamanigandan and A. A. Priscilla, "Analyzing the Performance of Novel Logistic Regression over Linear Regression Algorithms for Predicting Fake Job with Improved Accuracy," 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2024, pp. 1728-1732, doi: 10.1109/ICESC60852.2024.10690033.
  2. S. M. Reddy, S. Mohammed Ali, K. M. Battula, P. R. N. l. Charan and M. Rashmi, "Web App For Predicting Fake Job Posts Using Ensemble Classifiers," 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-5, doi: 10.1109/INCET57972.2023.10170567.
  3. S. R. Akiti, A. Bathini and S. K. Kanagala, "Random Forest based Fake Job Detection," 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN), Salem, India, 2024, pp. 427-430, doi: 10.1109/ICPCSN62568.2024.00073.
  4. G. Srinivas, A. Lakshmanarao, S. Sushma, M. V. Krishna and S. Neelima, "Fake News Detection Using ML and DL Approaches," 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India, 2023, pp. 1322-1325, doi: 10.1109/ICCPCT58313.2023.10245398.
  5. C. Prashanth, D. Chandrasekaran, B. Pandian, K. Duraipandian, T. Chen and M. Sathiyanarayanan, "Reveal: Online Fake Job Advert Detection Application using Machine Learning," 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022, pp. 1-6, doi: 10.1109/DELCON54057.2022.9752784.
  6. K. R. D. S. K. Karunanayake, K. T. M. Chandrawansa, L. C. T. Gunatilleke, K. Y. D. B. Menuranga, A. Gawesha and K. Manathunga, "ITConnect: Real-time Personalized Job Posting Platform for IT Professionals," 2023 8th International Conference on Information Technology Research (ICITR), Colombo, Sri Lanka, 2023, pp. 1-6, doi: 10.1109/ICITR61062.2023.10382750.
  7. B. Pandey, T. Kala, N. Bhoj, H. Gohel, A. Kumar and P. Sivaram, "Effective Identification of Spam Jobs Postings Using Employer Defined Linguistic Feature," 2022 1st International Conference on AI in Cybersecurity (ICAIC), Victoria, TX, USA, 2022, pp. 1-6, doi: 10.1109/ICAIC53980.2022.9897059.
  8. V. Gupta, R. S. Mathur, T. Bansal and A. Goyal, "Fake News Detection using Machine Learning," 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), Faridabad, India, 2022, pp. 84-89, doi: 10.1109/COM-IT-CON54601.2022.9850560.
  9. B. Umah, D. Gupta and M. Venugopalan, "Investigating Fake Job Descriptions with TF-IDF and Word Embeddings," 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 2024, pp. 1-6, doi: 10.1109/ICCCNT61001.2024.10725356.
  10. T. Bhatia and J. Meena, "Detection of Fake Online Recruitment Using Machine Learning Techniques," 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2022, pp. 300-304, doi: 10.1109/ICAC3N56670.2022.10074276.
  11. K. Akhila, M. A. Sofi, C. L. Reddy, A. Sarayu and M. Sannitya, "Improving Online Job Authenticity Detection Using Deep Learning and Focal Loss," 2024 International Conference on Data Science and Network Security (ICDSNS), Tiptur, India, 2024, pp. 1-8, doi: 10.1109/ICDSNS62112.2024.10691323.
  12. B. P. Prathaban, S. Rajendran, G. Lakshmi and D. Menaka, "Verification of Job Authenticity using Prediction of Online Employment Scam Model (POESM)," 2022 1st International Conference on Computational Science and Technology (ICCST), CHENNAI, India, 2022, pp. 1-6, doi: 10.1109/ICCST55948.2022.10040305.
  13. H. -Y. Suen, K. -E. Hung, C. -W. Liu, Y. -S. Su and H. -C. Fan, "Artificial Intelligence Can Recognize Whether a Job Applicant Is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers," in IEEE Transactions on Computational Social Systems, vol. 11, no. 5, pp. 5949-5960, Oct. 2024, doi: 10.1109/TCSS.2024.3376732.
  14. P. V. Chintalapati, S. S. Paluri, S. S. Nikhitha, T. S. V. Nanditha, S. T. V. L. Daneswari and P. K. Sree, "A Research Model For Automated Prediction And Analysis Of Job Interview Performance," 2024 Sixth International Conference on Computational Intelligence and Communication Technologies (CCICT), Sonepat, India, 2024, pp. 284-291, doi: 10.1109/CCICT62777.2024.00055.
  15. T. A. Yaphet, M. A. W. Putra, V. C. Avianny, I. S. Edbert and D. Suhartono, "Fake Job Vacancy Detection Using Ensemble Voting Classifier," 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA), Medan, Indonesia, 2024, pp. 1-7, doi: 10.1109/ICTIIA61827.2024.10761155.
  16. T. Sudhakar, B. C. Gogineni and J. Vijaya, "Fake Profile Identification Using Machine Learning," 2022 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Naya Raipur, India, 2022, pp. 47-52, doi: 10.1109/WIECON-ECE57977.2022.10150753.
  17. V. S. Gautam, S. Katiyar, K. Sharma and R. P. Ojha, "Social Media Fake News Detection Using Machine Learning," 2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2023, pp. 370-377, doi: 10.1109/ICAC3N60023.2023.10541571.
  18. A.Ferrari, "Artificial Intelligence in Engineering and Society: Blue Skies, Black Holes, and the Job of Requirements Engineers (Keynote)," 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), Hannover, Germany, 2023, pp. 67-67, doi: 10.1109/REW57809.2023.00018.
  19. I.Nessa et al., "Recruitment Scam Detection Using Gated Recurrent Unit," 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 2022, pp. 445-449, doi: 10.1109/R10-HTC54060.2022.9929928.
  20. N. Akram et al., "Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches," in IEEE Access, vol. 12, pp. 109388-109408, 2024, doi: 10.1109/ACCESS.2024.3435670.

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