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Smart Fraud Detection Framework for Job Recruitments

  • Research Article-Computer Engineering and Computer Science
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Abstract

Online recruitment has altered the hiring trend. Specifically, posting job ads on career portals and corporate sites involves seeking a large pool of professional applicants around the globe. Unfortunately, it has been established as another platform for fraudsters, which could lead to loss of privacy for applicants and damages organizations’ reputation. This case study handles the recruitment fraud/scam detection problem. Several important features of organization, job description and type of compensation are proposed and an effective recruitment fraud detection model is constructed using extreme gradient boosting method. It develops an algorithm that extracts required features from job ads and is tested using three examples. The features are further considered for two-step feature selection strategy. The findings show that features of the type of organization are most effective as a stand-alone model. The hybrid composition of selected 13 features demonstrated 97.94% accuracy and outperformed three state-of-the-art baselines. Moreover, the study finds that the most effective indicators are “salary_range,” “company_profile,” “organization_type,” “required education” and “has multiple jobs.” The findings highlight the number of research implications and provide new insights for detecting online recruitment fraud.

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Availability of Data and Material

The dataset is publicly available

Code Availability

The code of experiment is not available.

References

  1. Fred, M.O.; Kinange, U.M.: Effectiveness of E-recruitment in organization development. Manag Econ J, 294–301 (2018).

  2. Kumar, S.; Gupta, A.K.: A study on recruitment & selection process with reference. Int J Res 1, 1661–1666 (2014)

    Google Scholar 

  3. Nanda, S.K.; Mohanty, R.R.; Sukla, S.; Ghosh, G.C.: Development of intelligence process tracking system for job seekers. Int. J. Manag. Inf. Technol. 3(4), 57 (2011)

    Google Scholar 

  4. Ganesan, M.; Antony, S.P.; George, E.P.: Dimensions of job advertisement as signals for achieving job seeker’s application intention. J. Manag. Dev. 37(5), 425–438 (2018)

    Article  Google Scholar 

  5. Vidros, S.; Kolias, C.; Kambourakis, G.; Akoglu, L.: Automatic detection of online recruitment frauds: characteristics, methods, and a public dataset. Future Internet 9(1), 6 (2017)

    Article  Google Scholar 

  6. Mahbub, S.; Pardede, E.: Using contextual features for online recruitment fraud detection. (2018)

  7. Alghamdi, B.; Alharby, F.: An intelligent model for online recruitment fraud detection. J. Inf. Secur. 10(3), 155–176 (2019)

    Google Scholar 

  8. Lal, S.; Jiaswal, R.; Sardana, N.; Verma, A.; Kaur, A.; Mourya, R.: ORFDetector: ensemble learning based online recruitment fraud detection. In: 2019 Twelfth International Conference on Contemporary Computing (IC3) 2019, pp. 1–5. IEEE

  9. Vidros, S.; Kolias, C.; Kambourakis, G.: Online recruitment services: another playground for fraudsters. Comput Fraud Secur 2016(3), 8–13 (2016)

    Article  Google Scholar 

  10. Suryanto, T.; Rahim, R.,; Ahmar, A.S.: Employee recruitment fraud prevention with the implementation of decision support system. In: Journal of Physics: Conference Series 2018, vol. 1, p. 012055. IOP Publishing

  11. Kim, J.; Kim, H.-J.; Kim, H.: Fraud detection for job placement using hierarchical clusters-based deep neural networks. Appl Intell 49(8), 2842–2861 (2019)

    Article  Google Scholar 

  12. Dutta, S., Bandyopadhyay, S.K.: Fake job recruitment detection using machine learning approach

  13. Singh, S.; Kaur, S.: Improved spambase dataset prediction using svm Rbf kernel with adaptive boost. Int J Res Eng Technol 4(6), 383–386 (2015)

    Article  Google Scholar 

  14. Aski, A.S.; Sourati, N.K.: Proposed efficient algorithm to filter spam using machine learning techniques. Pac Sci Rev A: Nat Sci Eng 18(2), 145–149 (2016)

    Google Scholar 

  15. Alghoul, A.; Al Ajrami, S.; Al Jarousha, G.; Harb, G.; Abu-Naser, S.S.: Email classification using artificial neural network. (2018)

  16. Van Der Walt, E.; Eloff, J.: Using machine learning to detect fake identities: bots vs humans. IEEE Access 6, 6540–6549 (2018)

    Article  Google Scholar 

  17. Olatunji, S.O.: Improved email spam detection model based on support vector machines. Neural Comput. Appl. 31(3), 691–699 (2019)

    Article  Google Scholar 

  18. Abutair, H.Y.; Belghith, A.: Using case-based reasoning for phishing detection. Procedia Comput. Sci. 109, 281–288 (2017)

    Article  Google Scholar 

  19. Le, H.; Pham, Q.; Sahoo, D.; Hoi, S.C.: URLnet: learning a URL representation with deep learning for malicious URL detection. arXiv preprint arXiv:1802.03162 (2018)

  20. Mao, J.; Bian, J.; Tian, W.; Zhu, S.; Wei, T.; Li, A.; Liang, Z.: Detecting phishing websites via aggregation analysis of page layouts. Procedia Comput. Sci. 129, 224–230 (2018)

    Article  Google Scholar 

  21. Sahingoz, O.K.; Buber, E.; Demir, O.; Diri, B.: Machine learning based phishing detection from URLs. Expert Syst. Appl. 117, 345–357 (2019)

    Article  Google Scholar 

  22. de la Vega, L.G.M., Ng, V.: Modeling Trolling in Social Media Conversations. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018) (2018)

  23. Buckels, E.E.; Trapnell, P.D.; Andjelovic, T.; Paulhus, D.L.: Internet trolling and everyday sadism: parallel effects on pain perception and moral judgment. J. Pers. 87(2), 328–340 (2019)

    Article  Google Scholar 

  24. Akoglu, L.; Chandy, R.; Faloutsos, C.: Opinion fraud detection in online reviews by network effects. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)

  25. Feng, V.W.; Hirst, G.: Detecting deceptive opinions with profile compatibility. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing 2013, pp. 338–346

  26. Dong, M.; Yao, L.; Wang, X.; Benatallah, B.; Huang, C.; Ning, X.: Opinion fraud detection via neural autoencoder decision forest. Pattern Recognit. Lett. 132, 21–29 (2020)

    Article  Google Scholar 

  27. Durga, D.V.; Raju, Y.S.; Suribabu, D.: Design and analysis of user’s behavior in fraud detection in E-commerce websites

  28. http://icsdweb.aegean.gr/emscad. Accessed 12 January 2019 (2019)

  29. Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance 1994, pp. 1–5

  30. Motoda, H.; Liu, H.: Feature selection, extraction and construction. Commun IICM (Inst Inf Comput Mach Taiwan) 5(67–72), 2 (2002)

    Google Scholar 

  31. Yang, Y.; Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Icml 1997, vol. 412–420, p. 35

  32. Louppe, G.; Wehenkel, L.; Sutera, A.; Geurts, P.: Understanding variable importances in forests of randomized trees. In: Advances in neural information processing systems 2013, pp. 431–439

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan

    Asad Mehboob

  2. Department of Computer Science, Capital University of Science and Technology, Kahuta Road, Sihala, Islamabad, Islamabad Capital Territory, Pakistan

    M. S. I. Malik

Authors
  1. Asad Mehboob
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  2. M. S. I. Malik
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Correspondence to M. S. I. Malik.

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Mehboob, A., Malik, M.S.I. Smart Fraud Detection Framework for Job Recruitments. Arab J Sci Eng 46, 3067–3078 (2021). https://doi.org/10.1007/s13369-020-04998-2

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  • DOI: https://doi.org/10.1007/s13369-020-04998-2

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