Intrusion detection based on self-supervised graph neural networks and hybrid neural networks
To address the issues of limited feature extraction,strong data dependency,and poor generalization ability in existing network intrusion detection methods,this paper proposes an intrusion detection method based on self-supervised graph neural net-works and hybrid neural networks.Firstly,through a self-supervised learning strategy,a graph convolutional network is employed to extract structural features from network traffic data,enhancing the model's ability to learn features from unlabeled data.This re-duces dependence on labeled data and improves generalization ability.Secondly,a convolutional neural network is used to extract spatial features from the time series of network traffic,and a long short-term memory network is employed to model temporal de-pendencies,enabling multi-view feature extraction and improving detection comprehensiveness.Finally,a feature fusion strategy is designed to enrich the model's feature representation and enhance its robustness.Experimental results on public datasets dem-onstrate that the proposed method achieves higher accuracy and F1 score.
万方数据
维普
