The Support Vector Machine Model and Application Based on Particle Swarm Optimization Algorithm

The support vector machine (SVM) model has been widely used in network security fields since it was proposed, but the original SVM model has some defects. In response to the limitations of Support Vector Machine models, numerous experts and scholars have proposed a variety of optimization methods, which have achieved favorable applications. The performance of a conventional SVM largely depends on the selection of its parameters, and manually selecting these parameters is often a time-consuming and inaccurate process. Based on this, this paper optimizes the penalty parameter and the kernel parameter of the SVM using Particle Swarm Optimization (PSO), and applies it to the anomaly type detection in the KDDCUP dataset. Focusing on the intrusion detection problem of the KDDCUP99 dataset, this paper establishes a comparison between the conventional SVM model and the PSO-SVM model, analyzing the classification accuracy and convergence rate of the two models on the test set. The results show that the parameters obtained through Particle Swarm Optimization allow the retrained SVM to maintain sensitivity to abnormal traffic while reducing false positives for normal traffic. This indicates, demonstrating the effectiveness and practicality of the PSO-optimized SVM model. Furthermore, its application in network intrusion detection is expanded.