Semi-Supervised Learning With Interpolation and Pseudo-Labeling for Few-Label Intrusion Detection

Given the scarcity of labels in network traffic data, traditional supervised learning methods are limited by their dependence on large amounts of labeled data. While semi-supervised learning (SeSL) offers potential solutions, existing SeSL-based intrusion detection systems (IDS) still require substantial labeled samples for effective training, severely constraining their adaptability to emerging cyber threats under extreme label scarcity scenarios (e.g., 3-5 labels per class). This paper proposes IPL-SeSL, a novel SeSL framework that synergistically integrates Interpolation and Pseudo-Labeling mechanisms to enhance IDS’s performance under severe label constraints. IPL-SeSL consists of a supervised branch, a pseudo-labeling branch, and an interpolation branch. In the pseudo-labeling branch, we propose a data augmentation method specifically designed for network traffic data, which enhances the model’s robustness and generalization ability. The interpolation mechanism introduces a novel sample generation strategy that reinforces decision boundaries through geometrically meaningful feature space transformations. Comprehensive evaluations on the CICIoMT2024 benchmark demonstrate the framework’s exceptional performance, achieving 91% detection accuracy with merely 5 labeled instances per class.