An improved neural network intrusion detection method built upon support vector machines

Considering that current intrusion detection methods have low accuracy and long detection time when network traffic is classified, an improved neural network intrusion detection method based on support vector machine is proposed. Convolutional neural network extracts network traffic locally and deeply, and bidirectional gated recurrent unit extracts network traffic time sequence features. The two methods are combined to form a comprehensive feature extraction method with temporal memory function. Finally, the extracted features are classified by support vector machine instead of SoftMax activation function. According to the experimental results on the NSL-KDD dataset, the accuracy of CNN-BiGRU-SVM model is 99.67%, which is 25.35% higher than that of CNN-BiGRU-SoftMax model, effectively improving the accuracy of network traffic detection.