Tree-classification Algorithm to Ease User Detection of Predatory Hijacked Journals: Empirical Analysis of Journal Metrics Rankings

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Arnold Adimabua Ojugo 1,* Obinna Nwankwo 2

1. Department of Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria

2. Department of Computer Science, Novena University, Ogume, Delta State, Nigeria

* Corresponding author.


Received: 2 May 2021 / Revised: 1 Jun. 2021 / Accepted: 14 Jun. 2021 / Published: 8 Aug. 2020

Index Terms

Phishing, predatory journals, decision tree, tree algorithm, social engineering


A major challenge today in communication and over various communications medium is the wanton havoc wreaked by attackers as they continue to eavesdrop and intrude. Young and inexperienced academia are today faced with the challenge of journal houses to send cum have their articles published. The negative impact thus, of predatory and hijacked journals cannot be over-emphasized as adversaries use carefully crafted, social engineering (phishing attack) skills – to exploit unsuspecting and inexperienced academia usually for personals gains. These attacks re-direct victims to fake pages. The significance of the study is to advance a standard scheme/techniques employed by phished (predatory/hijacked) journals to scam young academia and inexperienced researchers in their quest for visibility in highly impactful indexed journals. Thus, our study advances a decision-tree algorithm that educates users by showing various indicators cum techniques advanced by predatory and hijacked journals. We explore journal phishing attacks employed by such journals, targeted at young academia to adequately differentiate also using web-page ranking. Results show the classification algorithm can effectively detect 95-percent accuracy of journal phishing based on journal metric indicators and website ranks.

Cite This Paper

Arnold Adimabua Ojugo, Obinna Nwankwo, " Tree-classification Algorithm to Ease User Detection of Predatory Hijacked Journals: Empirical Analysis of Journal Metrics Rankings ", International Journal of Engineering and Manufacturing (IJEM), Vol.11, No.4, pp. 1-9, 2021. DOI: 10.5815/ijem.2021.04.01


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