Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms

• URL: http://arxiv.org/abs/2111.13597v1
• Date: Fri, 26 Nov 2021 16:51:37 GMT
• Title: Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms
• Authors: Liyan Chang and Paula Branco
• Abstract summary: This paper presents two novel graph-based solutions for intrusion detection, the modified E-GraphSAGE, and E-ResGATalgorithms. The key idea is to integrate residual learning into the GNN leveraging the available graph information. An extensive experimental evaluation of four recent intrusion detection datasets shows the excellent performance of our approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The high volume of increasingly sophisticated cyber threats is drawing growing attention to cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection, new algorithms that are more robust, effective, and able to use more information are needed. Moreover, the intrusion detection task faces a serious challenge associated with the extreme class imbalance between normal and malicious traffics. Recently, graph-neural network (GNN) achieved state-of-the-art performance to model the network topology in cybersecurity tasks. However, only a few works exist using GNNs to tackle the intrusion detection problem. Besides, other promising avenues such as applying the attention mechanism are still under-explored. This paper presents two novel graph-based solutions for intrusion detection, the modified E-GraphSAGE, and E-ResGATalgorithms, which rely on the established GraphSAGE and graph attention network (GAT), respectively. The key idea is to integrate residual learning into the GNN leveraging the available graph information. Residual connections are added as a strategy to deal with the high-class imbalance, aiming at retaining the original information and improving the minority classes' performance. An extensive experimental evaluation of four recent intrusion detection datasets shows the excellent performance of our approaches, especially when predicting minority classes.

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