Name
Abstract | ||
|---|---|---|
The distributed and heterogeneous framework in the 5G heterogeneous network (Het-Net) makes it vulnerable to attacks of different kinds. Nodes for improving the network security are therefore important to eliminate such critical threats. Without cooperation or with limited cooperation, these nodes are substantially restricted in their protecting capacity due to specific characteristics such as heterogeneity, hierarchy, and wide range in the 5G HetNet. In this article, we propose a federated learning empowered end-edge-cloud cooperation-based framework for enhancing 5G HetNet security. In this framework, nodes equipped with attack detection mechanisms are distributed in the end, edge, and cloud of the 5G HetNet. We then design cooperative training schemes to realize the full potential of these nodes in detecting attacks. Illustrative results demonstrate the superior performance of our proposed scheme compared to three different benchmark schemes. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MNET.011.2000340 | IEEE Network |
Keywords | DocType | Volume |
cooperative training schemes,heterogeneous framework,distributed framework,attack detection mechanisms,5G HetNet security,federated learning empowered end-edge-cloud cooperation-based framework,network security,5G heterogeneous network | Journal | 35 |
Issue | ISSN | Citations |
2 | 0890-8044 | 3 |
PageRank | References | Authors |
0.46 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunkai Wei | 1 | 4 | 2.49 |
| Sipei Zhou | 2 | 3 | 0.79 |
| Supeng Leng | 3 | 3 | 1.81 |
| Sabita Maharjan | 4 | 3 | 0.46 |
| Yan Zhang | 5 | 5818 | 354.13 |