Network Security Protection System Based on Artificial Intelligence Algorithms

As network attacks become increasingly intelligent and covert, traditional network security systems that rely on rule matching or static feature detection, such as Snort, have become less effective in dealing with new risks such as advanced persistent threats and zero day attacks. Therefore, this study proposes a new network security protection framework based on artificial intelligence technology, using multi algorithm collaboration and multimodal fusion techniques. By optimizing the random forest algorithm, combining lightweight convolutional neural networks, and introducing a Transformer based multimodal fusion model, accurate identification, efficient classification, and real-time response to unknown threats are achieved. In the experimental validation phase, this study selected three publicly available datasets, KDD CUP 99, NSL-KDD, and CICIDS2017, for evaluation. The experimental results show that the comprehensive recognition accuracy of the proposed multimodal fusion model reaches 98.7%, which is about 5% to 8% higher than that of the single model; In the adversarial sample attack test, the false positive rate of the model was only 3.2%, and its stability was significantly better than the improved random forest algorithm (7.1%) and lightweight convolutional neural network (5.4%). In addition, due to the lightweight CNN having only about 2.3 million parameters, its inference delay is controlled within 5 milliseconds, demonstrating good adaptability to edge device deployment.