Name
Abstract | ||
|---|---|---|
Detecting abnormal service performance is significant for Internet-based service management and operation. Recent advances in anomaly detection methods prefer unsupervised learning algorithms since they can work without manually labelled data. However, existing unsupervised methods converge into suboptimal solutions due to their heuristic-based objectives. Moreover, they frequently rely on the str... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNSM.2021.3069225 | IEEE Transactions on Network and Service Management |
Keywords | DocType | Volume |
Training,Data models,Anomaly detection,Generative adversarial networks,Gallium nitride,Key performance indicator,Gaussian distribution | Journal | 18 |
Issue | ISSN | Citations |
2 | 1932-4537 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihai Nie | 1 | 0 | 1.01 |
| Laiping Zhao | 2 | 18 | 5.04 |
| Keqiu Li | 3 | 1415 | 162.02 |