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Journal Articles IEEE Access Year : 2023

Graph Neural Networks for Intrusion Detection: A Survey

Abstract

Cyberattacks represent an ever-growing threat that has become a real priority for most organizations. Attackers use sophisticated attack scenarios to deceive defense systems in order to access private data or cause harm. Machine Learning (ML) and Deep Learning (DL) have demonstrate impressive results for detecting cyberattacks due to their ability to learn generalizable patterns from flat data. However, flat data fail to capture the structural behavior of attacks, which is essential for effective detection. Contrarily, graph structures provide a more robust and abstract view of a system that is difficult for attackers to evade. Recently, Graph Neural Networks (GNNs) have become successful in learning useful representations from the semantic provided by graph-structured data. Intrusions have been detected for years using graphs such as network flow graphs or provenance graphs, and learning representations from these structures can help models understand the structural patterns of attacks, in addition to traditional features. In this survey, we focus on the applications of graph representation learning to the detection of network-based and host-based intrusions, with special attention to GNN methods. For both network and host levels, we present the graph data structures that can be leveraged and we comprehensively review the state-of-the-art papers along with the used datasets. Our analysis reveals that GNNs are particularly efficient in cybersecurity, since they can learn effective representations without requiring any external domain knowledge. We also evaluate the robustness of these techniques based on adversarial attacks. Finally, we discuss the strengths and weaknesses of GNN-based intrusion detection and identify future research directions.

Dates and versions

hal-04099607 , version 1 (17-05-2023)

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Cite

Tristan Bilot, Nour El Madhoun, Khaldoun Al Agha, Anis Zouaoui. Graph Neural Networks for Intrusion Detection: A Survey. IEEE Access, 2023, pp.1-1. ⟨10.1109/ACCESS.2023.3275789⟩. ⟨hal-04099607⟩
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