Multi modal data fusion defense strategy for campus network security: research on Kolmogorov Arnold Networks combined with B-spline function

This article proposes a Kolmogorov Arnold Networks (KANs) model that combines B-spline functions and is applied to a multimodal data fusion defense strategy for campus network security. Simulation experiments analyze the optimization effect of KANs models in various scenarios, including factors like network layers, adaptive learning rate, early stopping, L2 regularization, weighted loss function, and batch normalization. Experimental results show that, after optimizing the network structure and adopting adaptive learning rates, the KANs model converges more quickly and avoids overfitting. Especially with the introduction of early stopping strategy, L2 regularization, and Batch Normalization, the generalization ability of the model has been significantly improved. The strategy based on multimodal data fusion effectively improves the robustness and accuracy of the model when processing different types of data, such as images, text, time series data, etc. Experiments have shown that through weighted loss functions and multi task learning strategies, the model exhibits higher accuracy and lower false alarm rates when dealing with imbalanced samples and complex attack patterns. Overall, the proposed KANs model, combined with B-spline functions, not only optimizes campus network security defense but also improves the accuracy and robustness of multimodal data fusion tasks.