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

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Department of Information & Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece
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Computer Science Department, George Mason University, Fairfax, VA 22030, USA
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H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA 15213, USA
*
Author to whom correspondence should be addressed.
Current address: Laboratory of Information and Communication Systems Security, Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece.
Academic Editor: Dino Giuli
Future Internet 2017, 9(1), 6; https://doi.org/10.3390/fi9010006
Received: 16 December 2016 / Revised: 15 February 2017 / Accepted: 17 February 2017 / Published: 3 March 2017
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. View Full-Text
Keywords: fraud detection; online recruitment; employment scam; job scam; data mining; machine learning; natural language processing; dataset fraud detection; online recruitment; employment scam; job scam; data mining; machine learning; natural language processing; dataset
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MDPI and ACS Style

Vidros, S.; Kolias, C.; Kambourakis, G.; Akoglu, L. Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset. Future Internet 2017, 9, 6.

AMA Style

Vidros S, Kolias C, Kambourakis G, Akoglu L. Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset. Future Internet. 2017; 9(1):6.

Chicago/Turabian Style

Vidros, Sokratis; Kolias, Constantinos; Kambourakis, Georgios; Akoglu, Leman. 2017. "Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset" Future Internet 9, no. 1: 6.

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