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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM31256

Fake Job Detection Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM31256

Volume:

8 |

4 |

Apr 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM31256

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AR

Arikatla Rupasri

SR

Shaik Rehamunnisha

DSS

Dodda Sai Supriya

SMF

Shaik Mohammad Fahim

JL

Jammulamadaka Lakshmi

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Abstract

The research suggests using machine learning classification methods to identify and prevent fake job postings online. Nowadays, many companies prefer to advertise their job vacancies on the internet for easy access by job seekers. However, there are scammers who trick people into paying for non-existent jobs. Through data analysis and machine learning, we can distinguish between legitimate and fake job postings. Various algorithms are utilized to detect fraudulent posts and protect job seekers from falling victim to scams. The system is designed to teach the model how to distinguish between real and fake job listings using past data on fraudulent and legitimate job postings. Initially, supervised learning algorithms like classification techniques can be used to tackle the problem of identifying scammers in job ads. It will utilize multiple machine learning algorithms and choose the one with the best accuracy in predicting if a job posting is legitimate or not.

KEYWORDS

Fraud Job Job Seeker Machine Learning Internet Recruitment Classification

Cite This Article

Arikatla Rupasri, Shaik Rehamunnisha, Dodda Sai Supriya, Shaik Mohammad Fahim, Jammulamadaka Lakshmi, "Fake Job Detection Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 4, Apr 2024. https://doi.org/10.55041/IJSREM31256

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Publication Date

12/31/45402

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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