Detecting New Hijacked Journals by Using a List of Known Hijacked Journals and the Diagnosis of Web Domain Data

Abstract

Academia has faced the challenge of emerging hijacked journals, which create a fake website for a legitimate journal by copying its name, ISSN, and other metadata. Authors submitting a manuscript to a hijacked journal would not recognize that the journal website was for a hijacked journal instead of the legitimate one. There are various methods for detecting hijacked journals that are usable by information technology savvy researchers. Detected hijacked journals through use of these methods are usually then added to hijacked journals blacklists. This paper presents a new method for hijacked journals detection that uses the web domain data and list of known hijacked journals to identify new ones. By implementing this method, nine new hijacked journals were identified. This method can be used for detecting new hijacked journals and preventing additional victims–authors who submit papers to the hijacked instead of the legitimate journal.