The increasing sophistication of cyber threats and the complexity of modern network infrastructures have amplified the need for intelligent and adaptive intrusion detection systems (IDS). Traditional machine learning and deep learning methods often struggle to model the dynamic and relational characteristics inherent in network data. In this context, Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning structured representations from graph-based network traffic, enabling more accurate detection of malicious activities and anomalous behaviors. This paper presents a systematic review of advanced GNN techniques applied to network intrusion detection, encompassing architectural innovations, benchmark datasets, and performance trends reported across recent studies. The review follows a structured methodology, analyzing literature from 2018 to 2025 across major academic databases, and classifies existing approaches based on their graph modeling strategies, learning mechanisms, and detection objectives. Key findings indicate that GNN-based models significantly enhance detection precision, scalability, and resilience against evolving attack patterns. However, challenges remain in addressing explainability, computational efficiency, and real-time adaptability. The paper concludes by outlining future research directions, including the integration of explainable AI, federated learning frameworks, and hybrid GNN architectures to achieve interpretable, privacy-preserving, and adaptive intrusion detection solutions.

Keywords: Graph Neural Networks (GNNs), Intrusion Detection Systems (IDS), Cybersecurity, Network Analysis, Deep Learning, Threat Detection, Explainable AI