Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs
<|reference_start|>Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs: 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.<|reference_end|>
@article{gu2024always,
    title={Always be Pre-Training: Representation Learning for Network Intrusion
  Detection with GNNs},
    author={Zhengyao Gu, Diego Troy Lopez, Lilas Alrahis, and Ozgur Sinanoglu},
    journal={arXiv preprint arXiv:2402.18986},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18986},
    primaryClass={cs.CR}
}