ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection

Sangeeta Lal; Rishabh Jiaswal; Neetu Sardana; Ayushi Verma; Amanpreet Kaur; Rahul Mourya

doi:10.1109/IC3.2019.8844879

ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection

Sangeeta Lal, Rishabh Jiaswal, Neetu Sardana, Ayushi Verma, Amanpreet Kaur, Rahul Mourya

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 Citations (Scopus)

627 Downloads (Pure)

Abstract

Online recruitment fraud (ORF) is a new challenge in the cyber security area. In ORF, scammers give job seekers lucrative job offers and in-return steal their money and personal information. In India, scammers have stolen millions of moneys from innocent job seekers. Hence, it is important to find solution to this problem. In this paper, we propose, ORFDetector, an ensemble learning based model for ORF detection. We test the proposed model on publicly available dataset of 17,860 annotated jobs. The proposed model is found to be effective and give average f1-score and accuracy of 94% and 95.4, respectively. Additionally, it increases the specificity by 8% as compared to the baseline classifiers.

Original language	English
Title of host publication	2019 Twelfth International Conference on Contemporary Computing (IC3)
Editors	Sundaraja Sitharama Iyengar, Vikas Saxena
Publisher	IEEE
ISBN (Electronic)	9781728135915
DOIs	https://doi.org/10.1109/IC3.2019.8844879
Publication status	Published - 19 Sept 2019
Event	12th International Conference on Contemporary Computing 2019 - Noida, India Duration: 8 Aug 2019 → 10 Aug 2019

Publication series

Name	International Conference on Contemporary Computing
Publisher	IEEE
ISSN (Electronic)	2572-6129

Conference

Conference	12th International Conference on Contemporary Computing 2019
Abbreviated title	IC3 2019
Country/Territory	India
City	Noida
Period	8/08/19 → 10/08/19

Keywords

ensemble learning
machine leanring
Online fraud detection

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems and Management

Access to Document

10.1109/IC3.2019.8844879

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Lal, S., Jiaswal, R., Sardana, N., Verma, A., Kaur, A., & Mourya, R. (2019). ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection. In S. S. Iyengar, & V. Saxena (Eds.), 2019 Twelfth International Conference on Contemporary Computing (IC3) Article 8844879 (International Conference on Contemporary Computing). IEEE. https://doi.org/10.1109/IC3.2019.8844879

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title = "ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection",

abstract = "Online recruitment fraud (ORF) is a new challenge in the cyber security area. In ORF, scammers give job seekers lucrative job offers and in-return steal their money and personal information. In India, scammers have stolen millions of moneys from innocent job seekers. Hence, it is important to find solution to this problem. In this paper, we propose, ORFDetector, an ensemble learning based model for ORF detection. We test the proposed model on publicly available dataset of 17,860 annotated jobs. The proposed model is found to be effective and give average f1-score and accuracy of 94% and 95.4, respectively. Additionally, it increases the specificity by 8% as compared to the baseline classifiers.",

keywords = "ensemble learning, machine leanring, Online fraud detection",

author = "Sangeeta Lal and Rishabh Jiaswal and Neetu Sardana and Ayushi Verma and Amanpreet Kaur and Rahul Mourya",

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note = "12th International Conference on Contemporary Computing 2019, IC3 2019 ; Conference date: 08-08-2019 Through 10-08-2019",

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Lal, S, Jiaswal, R, Sardana, N, Verma, A, Kaur, A & Mourya, R 2019, ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection. in SS Iyengar & V Saxena (eds), 2019 Twelfth International Conference on Contemporary Computing (IC3)., 8844879, International Conference on Contemporary Computing, IEEE, 12th International Conference on Contemporary Computing 2019, Noida, India, 8/08/19. https://doi.org/10.1109/IC3.2019.8844879

ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection. / Lal, Sangeeta; Jiaswal, Rishabh; Sardana, Neetu et al.
2019 Twelfth International Conference on Contemporary Computing (IC3). ed. / Sundaraja Sitharama Iyengar; Vikas Saxena. IEEE, 2019. 8844879 (International Conference on Contemporary Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - ORFDetector

T2 - 12th International Conference on Contemporary Computing 2019

AU - Lal, Sangeeta

AU - Jiaswal, Rishabh

AU - Sardana, Neetu

AU - Verma, Ayushi

AU - Kaur, Amanpreet

AU - Mourya, Rahul

PY - 2019/9/19

Y1 - 2019/9/19

N2 - Online recruitment fraud (ORF) is a new challenge in the cyber security area. In ORF, scammers give job seekers lucrative job offers and in-return steal their money and personal information. In India, scammers have stolen millions of moneys from innocent job seekers. Hence, it is important to find solution to this problem. In this paper, we propose, ORFDetector, an ensemble learning based model for ORF detection. We test the proposed model on publicly available dataset of 17,860 annotated jobs. The proposed model is found to be effective and give average f1-score and accuracy of 94% and 95.4, respectively. Additionally, it increases the specificity by 8% as compared to the baseline classifiers.

AB - Online recruitment fraud (ORF) is a new challenge in the cyber security area. In ORF, scammers give job seekers lucrative job offers and in-return steal their money and personal information. In India, scammers have stolen millions of moneys from innocent job seekers. Hence, it is important to find solution to this problem. In this paper, we propose, ORFDetector, an ensemble learning based model for ORF detection. We test the proposed model on publicly available dataset of 17,860 annotated jobs. The proposed model is found to be effective and give average f1-score and accuracy of 94% and 95.4, respectively. Additionally, it increases the specificity by 8% as compared to the baseline classifiers.

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M3 - Conference contribution

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ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection

Abstract

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Keywords

ASJC Scopus subject areas

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