Intrusion Detection Model Based on Improved CBAM and BiGRU
Existing methods suffer from the problems of unbalanced network traffic data,insufficient detection accuracy,and an increasing false alarm rate.We propose a network intrusion detection model based on Improved CBAM(Convolutional Block Attention Module),dilated convolution and BiGRU(Bidirectional Gated Recurrent Unit),which aims to solve the problems of the existing methods.Specifically,to cope with the problem of unbalanced data distribution,we employ the ADASYN(Adaptive Oversampling)algorithm for adaptive oversampling to balance the dataset.To address the problems of insufficient detection accuracy and increasing false alarm rates,in the feature extraction phase,we first introduce a three-layer dilated convolution to expand the range of the sensing field so as to com-prehensively capture the features of network traffic.Second,we employ an improved CBAM module to enhance the extraction capability of dilated convolution for advanced features.Finally,BiGRU is also introduced to capture the long-term dependencies between features more deeply to further enhance the performance of the model.Experimental results show that the proposed method has a higher accuracy of 99.51%and a lower false detection rate of 2.90%relative to other methods on the NSL-KDD dataset,which suggests that the proposed model is a feasible and effective approach to network intrusion detection tasks.