Design of Network Attack Intrusion Detection System Based on Improved FWA Algorithm

The increasing diversity of network attack behaviors has led to increasingly serious network security issues. Based on this, this study proposes an optimized fireworks algorithm to build an intrusion detection model. Firstly, the traditional algorithm is optimized by improving the uniformity of initial individual distribution and designing a fitness value update strategy, which greatly reduces the computational burden of the model and improves recognition accuracy. Then, the feature analysis detection strategy is selected and the model is fused to ensure system stability. Finally, to validate the effectiveness of the model, a comparative experimental analysis is conducted. The results validated that the average accuracy of the research model was 99.06%, with an average detection rate of 96.98%, which is relatively higher than the other models by 2.57%. The error warning rate was only 0.13%, lower than the other models of 1.60%. In summary, the proposed intrusion detection model based on the fireworks algorithm and feature analysis can effectively identify attack behaviors and classify them correctly.