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