Network Intrusion Detection Using Transformer and BiGRU-DNN in Edge Computing
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초록
To address the issue of class imbalance in network traffic data, which affects the network intrusion detectionperformance, a combined framework using transformers is proposed. First, Tomek Links, SMOTE, and WGANare used to preprocess the data to solve the class-imbalance problem. Second, the transformer is used to encodetraffic data to extract the correlation between network traffic. Finally, a hybrid deep learning network modelcombining a bidirectional gated current unit and deep neural network is proposed, which is used to extract longdependencefeatures. A DNN is used to extract deep level features, and softmax is used to complete classification. Experiments were conducted on the NSLKDD, UNSWNB15, and CICIDS2017 datasets, and the detectionaccuracy rates of the proposed model were 99.72%, 84.86%, and 99.89% on three datasets, respectively. Compared with other relatively new deep-learning network models, it effectively improved the intrusiondetection performance, thereby improving the communication security of network data.
Abstract
To address the issue of class imbalance in network traffic data, which affects the network intrusion detectionperformance, a combined framework using transformers is proposed. First, Tomek Links, SMOTE, and WGANare used to preprocess the data to solve the class-imbalance problem. Second, the transformer is used to encodetraffic data to extract the correlation between network traffic. Finally, a hybrid deep learning network modelcombining a bidirectional gated current unit and deep neural network is proposed, which is used to extract longdependencefeatures. A DNN is used to extract deep level features, and softmax is used to complete classification. Experiments were conducted on the NSLKDD, UNSWNB15, and CICIDS2017 datasets, and the detectionaccuracy rates of the proposed model were 99.72%, 84.86%, and 99.89% on three datasets, respectively. Compared with other relatively new deep-learning network models, it effectively improved the intrusiondetection performance, thereby improving the communication security of network data.
참고문헌 (32)
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