10th WorldS4 2026

Authors - Joseph Afriyie, Stephen Opoku Oppong, Benjamin Ghansah, Daniel Kobina Danso Essel, Dickson Keddy Wornyo, Ephrem Kwaa Aidoo
Abstract - The COVID-19 pandemic forced educational institutions to adopt online learning, which resulted in expanded digital spaces that cybercriminals used to launch phishing attacks against students, faculty, and institutional systems. This research article provides a comprehen-sive literature review that evaluates machine learning techniques for phishing detection in online educational settings. The PRISMA guidelines were used to select 40 studies from 2013 to 2023 after researchers examined publications retrieved from IEEE Xplore, SpringerLink, and Google Scholar. The review analyzes various digital education ecosystems through its examination of algorithmic methods and datasets, performance evaluation metrics, and detection framework de-signs that universities use to defend against phishing attacks. The research shows that Random Forest and Gradient Boosting, together with deep learning methods, which include Convolutional Neural Networks, Long Short-Term Memory networks, and Recurrent Neural Networks, deliver superior detection performance reaching over 90% accuracy in most scenarios. Educational in-stitutions encounter three primary challenges, which include implementing real-time systems to combat emerging phishing techniques, ensuring dataset compatibility with various environments, and managing their restricted resource availability. The study establishes that institutions need to create technical detection frameworks that work together with user training programs to establish better institutional protection measures.