Communication Dans Un Congrès Année : 2023

Using Graph Neural Networks for the Detection and Explanation of Network Intrusions

Résumé

The ever-increasing evolution of Deep Learning methods has enabled their use in many areas, including cybersecurity. With the exponential growth in the volume of data circulating in computer networks, their security is a paramount necessity. Nowadays, network security is mainly pursued using preventive techniques but also by detecting intrusions as soon as possible, when they occur. Different types of Machine Learning and Deep Leaning models have been recently studied for network intrusion detection, but surprisingly, although Network Intrusion Detection Systems (NIDSs) scrutinize flow data exchanges on a network, graph-based models have been little explored so far. We propose in this article to consider the relevance of Graph Neural Network (GNNs) to detect intrusions and also to explain them. For this purpose, we adapt the GNNExplainer method, that is, the pioneer method for explaining GNN decisions, to edge-level classification models.

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hal-04909504 , version 1 (23-01-2025)

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Ahmed-Rafik-El Mehdi Baahmed, Giuseppina Andresini, C. Robardet, Annalisa Appice. Using Graph Neural Networks for the Detection and Explanation of Network Intrusions. Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of {ECML} {PKDD} 2023, Sep 2023, Turin, Italy. pp.201--216, ⟨10.1007/978-3-031-74633-8_13⟩. ⟨hal-04909504⟩
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