With the diversification of network attack means and the complication of traffic characteristics, the detection of network malicious traffic is facing increasingly severe challenges. Traditional traffic detection methods gradually fail to meet the needs of modern network environments in terms of accuracy and reliability, especially in the case of high-dimensional data and complex attack patterns. To address the above issues, this paper proposes a network malicious traffic detection model based on the Crested Porcupine Optimization Algorithm, Bidirectional Long Short-Term Memory Network, and Kolmogorov-Arnold Network. The model uses the Bidirectional Long Short-Term Memory Network to capture the bidirectional temporal features of traffic data, combines the nonlinear mapping of the Kolmogorov-Arnold Network to enhance feature expression capabilities, and optimizes hyperparameters through the Crested Porcupine Optimization Algorithm to improve model performance. Experiments are conducted using the CIC UNSW-NB15 enhanced dataset. The experimental results show that the model achieves accuracies of 99.12% and 94.15% in binary classification and multi-classification tasks, respectively, significantly outperforming other models. In addition, when dealing with class imbalance, the model particularly enhances the detection capability for minority class samples such as Backdoor and Worms.