  Graph neural network-based network intrusion detection systems have recently
demonstrated state-of-the-art performance on benchmark datasets. Nevertheless,
these methods suffer from a reliance on target encoding for data
pre-processing, limiting widespread adoption due to the associated need for
annotated labels--a cost-prohibitive requirement. In this work, we propose a
solution involving in-context pre-training and the utilization of dense
representations for categorical features to jointly overcome the
label-dependency limitation. Our approach exhibits remarkable data efficiency,
achieving over 98% of the performance of the supervised state-of-the-art with
less than 4% labeled data on the NF-UQ-NIDS-V2 dataset.
