A Bibliometric Analysis of Phishing in the Big Data Era: High Focus on Algorithms and Low Focus on People

• Published in: Procedia computer science 2023, Vol.219, p.91-98
• Main Authors: Pejić-Bach, Mirjana, Jajić, Ivan, Kamenjarska, Tanja
• Format: Article
• Language: English
• Subjects: bibliometric analysis; big data; co-occurrence; phishing; text-mining; VOSviewer
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2023.01.268

The phishing attacks, based on social engineering to persuade potential victims to provide valuable information, have significantly increased in the pandemic Covid-19 era, characterised by ubiquitous big data technologies. This paper aims to assess the theoretical and empirical research on phishing emails and big data that has been done to identify trends and recommend new areas for research. Using the VOSviewer program, the search results from the Web of Science (WoS) database were extracted. A mapping technique, using VoS Viewer, was used to examine articles on big data and phishing emails. The findings show that most of the field's research is carried out in nations in Asia and the United States of America and that the number of publications in this area is increasing exponentially. However, it is evident that researchers predominately concentrate on technical fields like computer science. Even though they are used in relatively small quantities, machine learning techniques, particularly artificial neural networks, are associated with most of the phishing publications that have been studied. Six clusters correspond to the main phishing domains: Phishing target or victim, Phishing channel, Big data analytics, Big data machine learning, Phishing attacker, and External phishing protection. The results indicate that real-time data collection and the development of effective algorithms are new approaches to combating phishing assaults. However, research outside of the technical domains is scarce.