<p>Deep learning has recently made significant advancements in intrusion detection. However, existing deep learning algorithms rely heavily on extensive data for training. It presents challenges when dealing with few-shot network traffic, resulting in low detection performance. To address the few-shot detection challenge, we propose a few-shot network intrusion detection method based on mutual centralized learning (FS-MCL). The method utilizes dense features extracted by an encoder and associates each feature with a particle in discrete space. This association allows the particle to randomly traverse the discrete feature space, establishing bidirectional associations between disjoint dense features. By measuring the expected visits of dense features in a Markov process, we can determine the probability of a query feature belonging to a support class. To address the scarcity of available few-shot datasets in intrusion detection, we also provide a visualization method that converts network traffic into image-like data, and we use traffic data from three public datasets to construct few-shot detection datasets to evaluate the proposed method. Experimental results demonstrate that the proposed method achieves excellent binary and multi-classification performance, with an average detection rate of up to 99.84%.</p>