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Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset

Author

Listed:
  • Sokratis Vidros

    (Department of Information & Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece
    Current address: Laboratory of Information and Communication Systems Security, Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece.)

  • Constantinos Kolias

    (Computer Science Department, George Mason University, Fairfax, VA 22030, USA)

  • Georgios Kambourakis

    (Department of Information & Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece
    Computer Science Department, George Mason University, Fairfax, VA 22030, USA)

  • Leman Akoglu

    (H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

Abstract

The critical process of hiring has relatively recently been ported to the cloud. Specifically, the automated systems responsible for completing the recruitment of new employees in an online fashion, aim to make the hiring process more immediate, accurate and cost-efficient. However, the online exposure of such traditional business procedures has introduced new points of failure that may lead to privacy loss for applicants and harm the reputation of organizations. So far, the most common case of Online Recruitment Frauds (ORF), is employment scam. Unlike relevant online fraud problems, the tackling of ORF has not yet received the proper attention, remaining largely unexplored until now. Responding to this need, the work at hand defines and describes the characteristics of this severe and timely novel cyber security research topic. At the same time, it contributes and evaluates the first to our knowledge publicly available dataset of 17,880 annotated job ads, retrieved from the use of a real-life system.

Suggested Citation

  • Sokratis Vidros & Constantinos Kolias & Georgios Kambourakis & Leman Akoglu, 2017. "Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset," Future Internet, MDPI, vol. 9(1), pages 1-19, March.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:1:p:6-:d:91991
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    Citations

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    Cited by:

    1. Stylianos S. Mamais & George Theodorakopoulos, 2017. "Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems," Future Internet, MDPI, vol. 9(4), pages 1-23, November.
    2. Frank Cremer & Barry Sheehan & Michael Fortmann & Arash N. Kia & Martin Mullins & Finbarr Murphy & Stefan Materne, 2022. "Cyber risk and cybersecurity: a systematic review of data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 698-736, July.

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