Research on intrusion detection method based on 1D-ICNN-BiGRU

Abstract

Intrusion detection is one of the effective ways to secure the network. Based the unbalanced network traffic data between different attacks, the intrusion detection algorithm turns out to be low intrusion detection accuracy and low identification rate of minority attacks. To solve the problems, an improved intrusion detection model combined with one-dimensional convolutional neural network and bidirectional gated recurrent unit(1D-ICNN-BiGRU) is proposed. First, the dataset is balanced by the SMOTE-Tomek algorithm, then 1D-ICNN-BiGRU is performed for feature extraction, and the final fully connected layer of the model is replaced by global average pooling. The results show that based on the balanced data, the proposed model achieves higher intrusion detection accuracy for R2L and U2R attack than other classic intrusion detection models. The proposed model also improved the identification rate of minority attacks, making the model accuracy rate reach 95.53%, and the false positive rate is only 2.98%.