摘要
Abstract
With the increasing number of network attacks,network traffic anomaly detection is becoming more and more important for maintaining network security and stability.However,existing methods are often difficult to effectively capture both static statistical features and dynamic temporal features of network traffic during feature extraction,resulting in limited detection performance in complex and evolving network environments.To address these issues,this paper proposes a two-channel multiscale gated fusion anomaly detection model(MSAD)based on semi-supervised learning.The model first extracts static statistical features of the traffic,including the number of packets,total bytes,etc.,through a multiscale convolutional neural network.Secondly,the temporal features of network traffic data are captured through a bidirectional GRU network and combined with a multi-head attention mechanism.Finally,adaptive fusion of different modal features is performed through gated fusion mechanism.Meanwhile,for the problem of insufficient credibility of pseudo-label generation in semi-supervised learning,a two-stage adversarial pseudo-label generation strategy is proposed,which effectively improves the robustness of pseudo-labels.The experimental results show that under the condition of limited labeled data,the model proposed in this paper achieves 99.63％,99.54％,99.9％and 99.72％of accuracy,precision,recall and F1 value on the CICIDS 2017 dataset,which is significantly better than traditional machine learning and deep learning methods.关键词
异常检测/半监督学习/多尺度卷积神经网络/双向GRU网络/门控融合机制Key words
anomaly detection/semi-supervised learning/multi-scale convolutional neural networks/bidirectional GRU networks/gated fusion mechanisms分类
信息技术与安全科学
