Detecting a network of hijacked journals by its archive

TL;DR

This paper presents a method to detect hijacked journals by analyzing clone journal archives, successfully identifying 62 hijacked URLs and predicting two clone sites before they went live, revealing networks of fraudulent organizers.

Contribution

Introduces an archive analysis approach to identify hijacked journal networks and predict clone websites, enhancing detection capabilities.

Findings

Detected 62 hijacked journal URLs

Predicted 2 clone websites before operation

Most hijacked journals form organized networks

Abstract

This study describes a method to detect hijacked journals based on the analysis of the archives of clone journals. This approach is most effective in discovering a network of hijacked journals that have the same organizer(s). Analysis of the archives of clone journals allowed to detect 62 URLs of hijacked journals. It also provided the possibility to predict two clone websites before they became operational. This study shows that most detected hijacked journals represent a network of clone journals organized by one or several fraudulent individuals. The information and content of nine legitimate journals were compromised in international and national scientometric databases.