An Intelligent Model for Online Recruitment Fraud Detection

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Journal of Information Security

ISSN Print: 2153-1234
ISSN Online: 2153-1242
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E-mail: jis@scirp.org

Google-based Impact Factor: 3.25
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"An Intelligent Model for Online Recruitment Fraud Detection"
written by Bandar Alghamdi, Fahad Alharby,
published by Journal of Information Security, Vol.10 No.3, 2019

has been cited by the following article(s):

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[31]	An investigation of crowdsourcing methods in enhancing the machine learning approach for detecting online recruitment fraud
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[35]	A Comparative Analysis of Fraudulent Recruitment Advertisement Detection Methods in the IoT Environment
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[36]	Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries
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[51]	Recruitment Fraud: Increased opportunities for exploitation in times of uncertainty?
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[53]	Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)
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[54]	Exploratory methods for imbalanced data classification in online recruitment fraud detection: A comparative analysis
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[58]	Spy the Lie: Fraudulent Jobs Detection in Recruitment Domain using Knowledge Graphs
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[59]	Performance Comparison of Two Class Boosted Decision Tree snd Two Class Decision Forest Algorithms in Predicting Fake Job Postings
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[62]	Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms
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[63]	Recruitment through Job Portals: A Critical Investigation of MBA Students in Pune City
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[66]	Fake Job Recruitment Detection Using Machine Learning Approach
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[67]	Breast Cancer Prognosis Using Stacking Ensemble
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[68]	FAKE JOB RECRUITMENT DETECTION USING MACHINE LEARNING
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[71]	JOB SCAM DETECTION USING MACHINE LEARNING APPROACH


[72]	To Study the Impact of Recruitment Scam on Potential Employees


[73]	A COMPARATIVE STUDY OF FAKE JOB POSTS USING DIFFERENT DATA MINING TECHNIQUES


[74]	THE EXAMINATION AND INVESTIGATION OF DECEPTIVE EMPLOYMENT POSTS BY UTILIZATION OF DIVERSE DATA MINING METHODOLOGIES


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[1]	Constructing an Investment Scam Detection Model Based on Emotional Fluctuations Throughout the Investment Scam Life Cycle Deviant Behavior, 2024 DOI:10.1080/01639625.2023.2244115

[2]	Fake Job Posting Detection International Journal of Advanced Research in Science, Communication and Technology, 2024 DOI:10.48175/IJARSCT-15950

[3]	Practical Applications of Data Processing, Algorithms, and Modeling Advances in Systems Analysis, Software Engineering, and High Performance Computing, 2024 DOI:10.4018/979-8-3693-2909-2.ch007

[4]	Predicting Fake Job Posts Using Machine Learning Models SSRN Electronic Journal, 2024 DOI:10.2139/ssrn.4807145

[5]	Practical Applications of Data Processing, Algorithms, and Modeling Advances in Systems Analysis, Software Engineering, and High Performance Computing, 2024 DOI:10.4018/979-8-3693-2909-2.ch007

[6]	Predicting Fake Job Posts Using Machine Learning Models SSRN Electronic Journal, 2024 DOI:10.2139/ssrn.4807145

[7]	Predicting Fake Job Posts Using Machine Learning Models SSRN Electronic Journal, 2024 DOI:10.2139/ssrn.4807318

[8]	Accelerating Discoveries in Data Science and Artificial Intelligence I Springer Proceedings in Mathematics & Statistics, 2024 DOI:10.1007/978-3-031-51167-7_1

[9]	Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches IEEE Access, 2024 DOI:10.1109/ACCESS.2024.3435670

[10]	Online Fake Logo Detection System 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), 2023 DOI:10.1109/CSCE60160.2023.00380

[11]	Online Fake Logo Detection System 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), 2023 DOI:10.1109/CSCE60160.2023.00380

[12]	A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques PeerJ Computer Science, 2023 DOI:10.7717/peerj-cs.1234

[13]	Predicting Fraudulant Job Ads with Machine Learning International Journal of Advanced Research in Science, Communication and Technology, 2023 DOI:10.48175/IJARSCT-9123

[14]	An investigation of crowdsourcing methods in enhancing the machine learning approach for detecting online recruitment fraud International Journal of Information Management Data Insights, 2023 DOI:10.1016/j.jjimei.2023.100167

[15]	Fake News Detection Using Machine Learning 2023 International Conference on Computer Communication and Informatics (ICCCI), 2023 DOI:10.1109/ICCCI56745.2023.10128339

[16]	Constructing an Investment Scam Detection Model Based on Emotional Fluctuations Throughout the Investment Scam Life Cycle Deviant Behavior, 2023 DOI:10.1080/01639625.2023.2244115

[17]	Fake News Detection Using ML and DL Approaches 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), 2023 DOI:10.1109/ICCPCT58313.2023.10245398

[18]	Fake News Detection Using Machine Learning 2023 International Conference on Computer Communication and Informatics (ICCCI), 2023 DOI:10.1109/ICCCI56745.2023.10128339

[19]	Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries IEEE Access, 2022 DOI:10.1109/ACCESS.2022.3197225

[20]	Reveal: Online Fake Job Advert Detection Application using Machine Learning 2022 IEEE Delhi Section Conference (DELCON), 2022 DOI:10.1109/DELCON54057.2022.9752784

[21]	A Comparative Study on the Prediction of Fake Job Posts using Various Data Mining Techniques International Journal of Advanced Research in Science, Communication and Technology, 2022 DOI:10.48175/IJARSCT-5733

[22]	Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries IEEE Access, 2022 DOI:10.1109/ACCESS.2022.3197225

[23]	Effective Identification of Spam Jobs Postings Using Employer Defined Linguistic Feature 2022 1st International Conference on AI in Cybersecurity (ICAIC), 2022 DOI:10.1109/ICAIC53980.2022.9897059

[24]	A Comparative Analysis of Fraudulent Recruitment Advertisement Detection Methods in the IoT Environment Journal of Sensors, 2022 DOI:10.1155/2022/4583512

[25]	Recruitment Scam Detection Using Gated Recurrent Unit 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), 2022 DOI:10.1109/R10-HTC54060.2022.9929928

[26]	Exploratory Methods for Imbalanced Data Classification in Online Recruitment Fraud Detection: A Comparative Analysis 2021 4th International Conference on Computing and Big Data, 2021 DOI:10.1145/3507524.3507537

[27]	Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM) International Journal of Web-Based Learning and Teaching Technologies, 2021 DOI:10.4018/IJWLTT.287096

[28]	Detecting Online Recruitment Fraud Using Machine Learning 2021 9th International Conference on Information and Communication Technology (ICoICT), 2021 DOI:10.1109/ICoICT52021.2021.9527477

[29]	Online Recruitment Fraud Detection using ANN 2021 Palestinian International Conference on Information and Communication Technology (PICICT), 2021 DOI:10.1109/PICICT53635.2021.00015

[30]	A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2021 DOI:10.1109/ICREST51555.2021.9331230

[31]	Exploratory Methods for Imbalanced Data Classification in Online Recruitment Fraud Detection: A Comparative Analysis 2021 4th International Conference on Computing and Big Data, 2021 DOI:10.1145/3507524.3507537

[32]	Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM) International Journal of Web-Based Learning and Teaching Technologies, 2021 DOI:10.4018/IJWLTT.287096

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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