Investigation and rectification of NIDS datasets and standratized feature set derivation for network attack detection with graph neural networks. (arXiv:2212.13994v1 [cs.CR])

Network Intrusion and Detection Systems (NIDS) are essential for malicious
traffic and cyberattack detection in modern networks. Artificial
intelligence-based NIDS are powerful tools that can learn complex data
correlations for accurate attack prediction. Graph Neural Networks (GNNs)
provide an opportunity to analyze network topology along with flow features
which makes them particularly suitable for NIDS applications. However,
successful application of such tool requires large amounts of carefully
collected and labeled data for training and testing. In this paper we inspect …

attack datasets detection investigation network networks neural networks