Self-Organizing Graph Neural Networks for Network Intrusion Detection

Abstract

Network intrusion detection systems (NIDS) face increasing challenges due to the dynamic and evolving nature of cyber threats. Traditional graph neural network (GNN) approaches often struggle to adapt effectively to these changes, relying on static graph structures and pre-defined node features. This paper proposes a novel self-organizing GNN architecture designed specifically for network intrusion detection. The core idea is to enable the GNN to continuously learn and adapt its node representations and connection topology based on real-time network traffic patterns and intrusion detection alerts. This dynamic adaptation allows the system to respond effectively to emerging threats and maintain high detection accuracy even in highly volatile network environments. The proposed architecture incorporates a feedback loop that utilizes detected intrusions to refine the graph structure and node embeddings, creating a resilient and adaptive NIDS. We demonstrate the potential of this approach through a theoretical framework and outline key design considerations, providing a foundation for future research and development in this critical area. The central contribution is a method for dynamically updating the GNN's representation of the network, addressing a major limitation of static GNN models in the context of cybersecurity.