Anomaly Detection Method of Network Traffic Based on MHA-BiLSTM
A network traffic anomaly detection method based on MHA and BiLSTM connected with fused Highway is proposed to address the issues of low identification accuracy and neglect of inter-feature relationships in traditional network traffic anomaly detection methods.Through utilizing the MHA mechanism to learn feature relationships among data,the feature relationships of different dimensions are extracted.Subsequently,multiple layers of BiLSTM are employed for capturing long-term dependency features,while the Highway connections are utilized to alleviate the issue of gradient vanishing in deep network training.The accuracy and effectiveness of the proposed method are validated through the NSL-KDD dataset.
国家科技期刊平台
NSTL
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