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 proceedingConference contribution

57 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 languageEnglish
Title of host publication2019 Twelfth International Conference on Contemporary Computing (IC3)
EditorsSundaraja Sitharama Iyengar, Vikas Saxena
PublisherIEEE
ISBN (Electronic)9781728135915
DOIs
Publication statusPublished - 19 Sep 2019
Event12th International Conference on Contemporary Computing 2019 - Noida, India
Duration: 8 Aug 201910 Aug 2019

Publication series

NameInternational Conference on Contemporary Computing
PublisherIEEE
ISSN (Electronic)2572-6129

Conference

Conference12th International Conference on Contemporary Computing 2019
Abbreviated titleIC3 2019
CountryIndia
CityNoida
Period8/08/1910/08/19

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

Fingerprint Dive into the research topics of 'ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection'. Together they form a unique fingerprint.

Cite this