The performance of network traffic detection heavily relies on high-quality annotated data, yet the widespread presence of noisy labels in real-world scenarios severely undermines model reliability and generalization. Existing methods predominantly rely on training dynamics signals and struggle to distinguish between noisy labels and valuable hard samples, leading to diminished model sensitivity to emerging threats. To address this fundamental challenge, this paper proposes a noise-tolerant label recognition and correction framework based on graph-structured neighbor consistency (NCRT). The framework leverages the inherent clustering characteristics of network traffic in feature space, constructs an adaptive K-nearest neighbor graph to model behavioral similarities among samples, and employs multi-dimensional consistency evaluation metrics to accurately differentiate between noisy labels and hard samples. Furthermore, a feature-space-based label propagation algorithm is introduced, which utilizes reliable anchor samples in the graph to guide noisy labels toward correction based on the consensus of their semantic neighbors. The core innovation lies in leveraging graph-structured neighbor consistency to fundamentally distinguish noisy labels from hard samples, and correcting them via label propagation over an adaptive K-nearest neighbor graph. Experiments on real-world datasets such as CICIDS2017 and NSL-KDD demonstrate that the proposed method significantly outperforms mainstream baseline approaches in terms of noise identification, label correction, and final classification performance. It exhibits exceptional robustness and stability, particularly under high-ratio and asymmetric noise settings, providing an effective solution for building reliable traffic detection models adapted to complex real-world network environments.
Yang et al. (Thu,) studied this question.