Journal of Information Security

Volume 10, Issue 3 (July 2019)

ISSN Print: 2153-1234   ISSN Online: 2153-1242

Google-based Impact Factor: 3.79  Citations  

An Intelligent Model for Online Recruitment Fraud Detection

HTML  XML Download Download as PDF (Size: 1239KB)  PP. 155-176  
DOI: 10.4236/jis.2019.103009    2,939 Downloads   9,349 Views  Citations

ABSTRACT

This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.

Share and Cite:

Alghamdi, B. and Alharby, F. (2019) An Intelligent Model for Online Recruitment Fraud Detection. Journal of Information Security, 10, 155-176. doi: 10.4236/jis.2019.103009.

Cited by

[1] A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques
PeerJ Computer Science, 2023
[2] Online Fake Logo Detection System
2023
[3] FAKE JOB DETECTION USING MACHINE LEARNING APPROACH
Journal of …, 2023
[4] Machine Learning Techniques for True and Fake Job Posting
ECS Transactions, 2022
[5] A machine learning approach to detecting fraudulent job types
AI & SOCIETY, 2022
[6] Reveal: Online Fake Job Advert Detection Application using Machine Learning
2022 IEEE Delhi …, 2022
[7] Effective Identification of Spam Jobs Postings Using Employer Defined Linguistic Feature
… Conference on AI in …, 2022
[8] Online fake job advertisement recognition and classification using machine learning
3 c TIC: cuadernos de desarrollo aplicados a las …, 2022
[9] ONLINE JOB SCAM DETECTION USING SMART SYSTEM
2022
[10] A Comparative Analysis of Fraudulent Recruitment Advertisement Detection Methods in the IoT Environment
Journal of Sensors, 2022
[11] Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries
IEEE Access, 2022
[12] Detecting fraud job recruitment using features reflecting from real-world knowledge of fraud
intr - CURRENT APPLIED SCIENCE …, 2022
[13] Recruitment Scam Detection Using Gated Recurrent Unit
2022 IEEE 10th …, 2022
[14] Detecting online recruitment fraud by using machine learning
2021
[15] Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)
International Journal of Web-Based Learning …, 2021
[16] Exploratory methods for imbalanced data classification in online recruitment fraud detection: A comparative analysis
… 4th International Conference on Computing and …, 2021
[17] Online recruitment fraud detection using ANN
… Conference on Information …, 2021
[18] Detecting online recruitment fraud using machine learning
2021 9th International …, 2021
[19] Artificial intelligence based Fake Job Recruitment Detection Using Machine Learning Approach
2021
[20] Spy the Lie: Fraudulent Jobs Detection in Recruitment Domain using Knowledge Graphs
2021
[21] Performance Comparison of Two Class Boosted Decision Tree snd Two Class Decision Forest Algorithms in Predicting Fake Job Postings
2021
[22] Ensemble Modeling on Job Scam Detection
2021
[23] A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques
2021
[24] Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms
2021
[25] Recruitment Fraud: Increased opportunities for exploitation in times of uncertainty?
Smith - Social Alternatives, 2021
[26] Recruitment through Job Portals: A Critical Investigation of MBA Students in Pune City
2020
[27] Smart Fraud Detection Framework for Job Recruitments
2020
[28] Flood Inundation Mapping of Cempaka Tropical Cyclone Effect in Gunung Sewu Karst Landscape Using Sentinel 1
2020
[29] Fake Job Recruitment Detection Using Machine Learning Approach
2020
[30] Breast Cancer Prognosis Using Stacking Ensemble
2020
[31] Supervisors: JN van Rijn (LIACS) & A. Mashaghi Tabari (LACDR)
[32] FAKE JOB RECRUITMENT DETECTION USING MACHINE LEARNING
[33] Classification of Genuinity in Job Posting Using Machine Learning
[34] Text Mining im Personalmanagement

Journals Menu
Follow SCIRP
Contact us
+1 323-425-8868
customer@scirp.org
WhatsApp +86 18163351462(WhatsApp)
Click here to send a message to me 1655362766
Paper Publishing WeChat

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.