信息安全研究2024，Vol.10Issue(3)：202-208,7.DOI:10.12379/j.issn.2096-1057.2024.03.02
融合CNN-BiGRU和注意力机制的网络入侵检测模型
A Network Intrusion Detection Model Integrating CNN-BiGRU and Attention Mechanism
摘要
Abstract
To enhance the feature extraction capabilities and classification accuracy of the network intrusion detection model,a network intrusion detection model integrating CNN-BiGRU(Convolutional Neural Network-Bi-directional Gated Recurrent Unit)and attention mechanism is proposed.CNN is employed to effectively extract nonlinear features from traffic datasets,while BiGRU extracts time-series features.The attention mechanism is then integrated to differentiate the importance of different types of traffic data through weighted means,thereby improvingthe overall performance of the model in feature extraction and classification.The experimental results indicate that the overall accuracy rate is 2.25％higher than that of the BiLSTM(Bi-directional Long Short-Term Memory)model.K-fold cross-validation results demonstrate that the proposed model's good generalization performance,avoiding the occurrence of over-fitting phenomenon,and affirming its effectiveness and rationality.关键词
网络入侵检测/卷积神经网络/双向门控循环单元/注意力机制/深度学习Key words
network intrusion detection/convolutional neural network/bidirectional gated recurrent unit/attention mechanism/deep learning分类
信息技术与安全科学引用本文复制引用
杨晓文,张健,况立群,庞敏..融合CNN-BiGRU和注意力机制的网络入侵检测模型[J].信息安全研究,2024,10(3):202-208,7.基金项目
国家自然科学基金项目(62272426,62106238) （62272426,62106238）
山西省科技成果转化引导专项(202104021301055) （202104021301055）
