Dec 12, 2022 · To address these issues, we propose a dynamic line graph neural network (DLGNN)-based intrusion detection method with semisupervised learning.

A dynamic line graph neural network (DLGNN)-based intrusion detection method with semisupervised learning that converts network traffic into a series of ...

To address these issues, we propose a dynamic line graph neural network (DLGNN)-based intrusion detection method with semisupervised learning. Our model ...

Dec 20, 2022 · To address these issues, we propose a dynamic line graph neural network (DLGNN)-based intrusion detection method with semisupervised learning.

Mar 3, 2024 · This paper studies the application of GNNs to identify the specific types of network flows in an unsupervised manner.

This proposal is the first successful, practical, and extensively evaluated approach of applying GNNs on the problem of network intrusion detection for IoT ...

To address these issues, we propose a dynamic line graph neural network (DLGNN)-based intrusion detection method with semisupervised learning. Our model ...

Duan et al. Application of a dynamic line graph neural network for intrusion detection with semisupervised learning. IEEE Trans. Inf. Forensics Secur. (2023).

Jun 1, 2023 · 首先，NIDS 模型将网络流量展开为一系列离散的时空图。接下来，NIDS 模型通过结合网络流量的统计数据和拓扑信息提取每个图快照的空间特征。在此基础上，NIDS ...

By learning network connectivity, graph neural networks can quantify the importance of neighboring nodes and node features to make more reliable predictions.