Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms
This work addresses the problem of extreme class imbalance in intrusion detection for cybersecurity applications, representing an incremental advancement by adapting existing GNN methods with residual connections.
The paper tackled intrusion detection in cybersecurity by proposing two graph-based algorithms, modified E-GraphSAGE and E-ResGAT, which integrated residual learning to address class imbalance, resulting in improved performance, especially for minority classes, as demonstrated on four recent datasets.
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.