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The transportation information security system plays an important role in the development of traffic information building. The intrusion detection rate of the transportation information security system is often affected by the structure parameter design of the support vector machine (SVM) model. Improper SVM model design may lead to a low detection precision. To overcome these problems, a new intrusion detection method based on kernel principal component analysis (KPCA), Particle swarm optimization (PSO) and SVM is proposed in this paper. The KPCA was adopted to obtain the useful features of the original data to eliminate the redundancy. Then the PSO was employed to optimize the training procedure of the SVM. Thus, satisfactory SVM model with good extendable ability was attained. The efficiency of the proposed method was evaluated with the KDD dataset. The experiment results demonstrate that the proposed approach outperforms the existing methods, and thus is available for the transportation information security.