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Deep learning-based fine-grained network intrusion detection systems (NIDS)enable different attacks to be responded to in a fast and targeted manner withthe help of large-scale labels. However, the cost of labeling causesinsufficient labeled samples. Also, the real fine-grained traffic shows along-tailed distribution with great class imbalance. These two problems oftenappear simultaneously, posing serious challenges to fine-grained NIDS. In thiswork, we propose a novel semi-supervised fine-grained intrusion detectionframework, SF-IDS, to achieve attack classification in the label-limited andhighly class imbalanced case. We design a self-training backbone model calledRI-1DCNN to boost the feature extraction by reconstructing the input samplesinto a multichannel image format. The uncertainty of the generatedpseudo-labels is evaluated and used as a reference for pseudo-label filteringin combination with the prediction probability. To mitigate the effects offine-grained class imbalance, we propose a hybrid loss function combiningsupervised contrastive loss and multi-weighted classification loss to obtainmore compact intra-class features and clearer inter-class intervals.Experiments show that the proposed SF-IDS achieves 3.01% and 2.71% Marco-F1improvement on two classical datasets with 1% labeled, respectively.

SF-IDS: An Imbalanced Semi-Supervised Learning Framework for Fine-grained Intrusion Detection