Research on Hybrid Service Classification Based on KNN-NB

In the current environment of increasingly complex network services, accurate service classification is crucial for network management and optimization. This paper proposes a hybrid service classification method that integrates the K-Nearest Neighbor (KNN) algorithm and the Naive Bayes (NB) algorithm. By constructing a “three-layer progressive” architecture, the method achieves complementary advantages between local feature response and global probabilistic reasoning through feature preprocessing, heterogeneous algorithm fusion, and dynamic decision support. Experiments on the NSL-KDD dataset and self-collected three-type service data demonstrate that the classification accuracy of the hybrid model reaches 0.8689, significantly outperforming single KNN, NB, and other comparative algorithms.