A timestamp-aware spatio-temporal graph contrastive learning model for network intrusion detection outperforms other self-supervised methods on four datasets while matching supervised GNN performance.
However, mostexisting models are either developed for general dynamicgraph learning or tailored for some special time series pre-diction
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Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection
A timestamp-aware spatio-temporal graph contrastive learning model for network intrusion detection outperforms other self-supervised methods on four datasets while matching supervised GNN performance.