Rademics Research Institute

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for enhancing network intrusion detection systems (IDS) by leveraging the structural relationships inherent in network data. This chapter explores the integration of GNNs into cybersecurity, focusing on their application to network intrusion detection and vulnerability analysis. GNN-based models enable efficient identification of complex attack patterns, including botnets, DDoS, and insider threats, by capturing dynamic interactions between nodes in a network. The chapter delves into key aspects such as graph construction from real-world network data, the role of community detection for identifying malicious clusters, and the comparative analysis of various GNN architectures. Additionally, it addresses the deployment challenges of real-time intrusion detection, emphasizing scalability, latency, and adaptability. By providing an in-depth understanding of GNN applications and challenges, this chapter contributes valuable insights to advancing intrusion detection technologies in the evolving landscape of network security.

Introduction

In the modern digital landscape, the complexity and sophistication of cyberattacks continue to evolve, presenting a significant challenge for traditional network security solutions [1]. Traditional intrusion detection systems (IDS) primarily rely on signature-based detection methods, which often fail to identify novel or zero-day attacks [2-4]. The rapid growth of interconnected devices, coupled with the increasing use of dynamic and decentralized network topologies, has created a fertile ground for advanced persistent threats (APTs), botnets, and other malicious activities [5-7]. These challenges demand innovative approaches to enhance the accuracy and efficiency of network security systems [8]. One such promising approach was the application of Graph Neural Networks (GNNs), which have the ability to model complex relationships between entities within a network and detect unusual patterns thatotherwise go unnoticed [9]. GNNs offer the potential to revolutionize the way network intrusion detection systems operate by providing a more robust and adaptive mechanism for identifying cyber threats in real-time [10].

The core strength of GNNs lies in their ability to process and analyze graph-structured data [11]. Networks, by nature, are represented as graphs where nodes represent devices or entities, and edges represent the interactions or communication links between them [12]. In the context of cybersecurity, such graph representations can capture intricate details about network traffic and behavior that traditional IDS techniquesoverlook [13]. For instance, a GNN can track the flow of data between nodes, detect abnormal patterns in node interactions, and flag potential vulnerabilities [14-16]. This graph-based representation allows GNNs to model not just the direct connections between nodes but also the higher-order relationships, enabling the identification of hidden attack paths, complex multi-stage attacks, and anomalies thatotherwise be difficult to detect using conventional methods [17-19]. As cyber threats become more advanced and unpredictable, GNNs provide a powerful tool for understanding and securing network infrastructures [20].