Systematic Review of Graph Neural Network for Malicious Attack Detection
Sarah Mohammed Alshehri, Sanaa Sharaf, Rania Molla
Abstract
As cyberattacks continue to rise alongside the rapid expansion of digital systems, effective threat detection remains a critical yet challenging task. While several machine learning approaches have been proposed, the use of graph neural networks (GNNs) for cyberattack detection has not yet been systematically explored in depth. This paper presents a systematic literature review (SLR) that analyzes 28 recent academic studies published between 2020 and 2025, retrieved from major databases including IEEE, ACM, Scopus, and Springer. The review focuses on evaluating how GNN models are applied in detecting various types of attacks, particularly those targeting IoT environments, web services, phishing, and network traffic. Studies were classified based on the type of dataset, GNN model architecture, and attack domain. Additionally, key limitations and future research directions were extracted and analyzed. The findings provide a structured comparison of current methodologies and highlight gaps that warrant further exploration. This review contributes a focused perspective on the potential of GNNs in cybersecurity and offers insights to guide future developments in the field.