Name
Abstract | ||
|---|---|---|
5G technologies provide ubiquitous connectivity. However, 5G security is a particularly important issue. Moreover, because public datasets are outdated, we need to create a self-generated dataset on the virtual platform. Therefore, we propose a two-stage intelligent detection model to enable 5G networks to withstand security issues and threats. Finally, we define malicious traffic detection capability metrics. We apply the self-generated dataset and metrics to thoroughly evaluate the proposed mechanism. We compare our proposed method with benchmark statistics and neural network algorithms. The experimental results show that the two-stage intelligent detection model can distinguish between benign and abnormal traffic and classify 21 kinds of DDoS. Our analysis also shows that the proposed approach outperforms all the compared approaches in terms of detection rate, malicious traffic detection capability, and response time. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22072532 | SENSORS |
Keywords | DocType | Volume |
malicious behavior, statistic model, neural network model, DDoS | Journal | 22 |
Issue | ISSN | Citations |
7 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Man Li | 1 | 0 | 0.68 |
| Huachun Zhou | 2 | 370 | 54.39 |
| Yajuan Qin | 3 | 187 | 21.81 |