Optimizing IoT Intrusion Detection—A Graph Neural Network Approach with Attribute-Based Graph Construction

Abstract

The inherent complexity and heterogeneity of the Internet of Things (IoT) ecosystem present significant challenges for developing effective intrusion detection systems. While graph deep-learning-based methods have shown promise in cybersecurity applications, existing approaches primarily construct graphs based on physical network connections, which may not effectively capture node representations. This paper proposes a Top-K Similarity Graph Framework (TKSGF) for IoT network intrusion detection. Instead of relying on physical links, the TKSGF constructs graphs based on Top-K attribute similarity, ensuring a more meaningful representation of node relationships. We employ GraphSAGE as the Graph Neural Network (GNN) model to effectively capture node representations while maintaining scalability. Furthermore, we conducted extensive experiments to analyze the impact of graph directionality (directed vs. undirected), different K values, and various GNN architectures and configurations on detection performance. Evaluations on binary and multi-class classification tasks using the NF-ToN IoT and NF-BoT IoT datasets from the Machine-Learning-Based Network Intrusion Detection System (NIDS) benchmark demonstrated that our proposed framework consistently outperformed traditional machine learning methods and existing graph-based approaches, achieving superior classification accuracy and robustness.

Keywords

• network intrusion detection system
• graph neural network
• similarity-based graph construction
• binary classification
• multi-class classification