E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

Abstract

This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can leverage the inherent structure of graph-based data. Training and evaluation data for NIDSs are typically represented as flow records, which can naturally be represented in a graph format. In this paper, we propose E-GraphSAGE, a GNN approach that allows capturing both the edge features of a graph as well as the topological information for network intrusion detection in IoT networks. To the best of our knowledge, our proposal is the first successful, practical, and extensively evaluated approach of applying GNNs on the problem of network intrusion detection for IoT using flow-based data. Our extensive experimental evaluation on four recent NIDS benchmark datasets shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of GNNs in network intrusion detection, and provides motivation for further research.

will appear in IEEE/IFIP Network Operations and Management Symposium 2022

References in corpus (2)

• An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
• NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems

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• X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System
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• Identification of Device Dependencies Using Link Prediction
• Defending Network Intrusion Detection Systems Based on Graph Neural Networks Against Structural Adversarial Attacks