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

Sokratis Vidros, Constantinos Kolias, Georgios Kambourakis and Leman Akoglu
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Sokratis Vidros: Department of Information & 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
Leman Akoglu: H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Future Internet, 2017, vol. 9, issue 1, 1-19

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.

Keywords: fraud detection; online recruitment; employment scam; job scam; data mining; machine learning; natural language processing; dataset (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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