Phishing Detection Framework and Cybersecurity Effectiveness during COVID-19 pandemic: A Technical Review

Granados D.A.B.; Paucar J.V.G.; Alvarez S.R.R.; Cornejo J. · ISMSIT 2025 - 9th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings (2025) · DOI: 10.1109/ismsit67332.2025.11268031

Resumen

The COVID-19 pandemic triggered a significant surge in phishing attacks by exploiting increased reliance on digital services and widespread misinformation, yet existing reviews remain confined to traditional detection methods - such as blacklists and static signatures - without thoroughly assessing advanced machine learning techniques to counter this cyber threat. Furthermore, these studies fail to compare framework effectiveness across different regions or organizational settings, nor do they propose adaptable solutions for crisis scenarios; this systematic review addresses these gaps through a comprehensive analysis of current detection approaches, emphasizing machine learning algorithms and their applicability in remote work environments to provide actionable recommendations for enhancing phishing resilience while considering technical, geographical, and organizational factors in an era of accelerated digital transformation.

Instituciones: Universidad Tecnológica del Perú