Name
Abstract | ||
|---|---|---|
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this article... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TDSC.2021.3063947 | IEEE Transactions on Dependable and Secure Computing |
Keywords | DocType | Volume |
Noise measurement,Data models,Anomaly detection,Predictive models,Task analysis,Face recognition,Machine learning algorithms | Journal | 18 |
Issue | ISSN | Citations |
5 | 1545-5971 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zilong Zhao | 1 | 3 | 4.45 |
| Robert Birke | 2 | 143 | 17.83 |
| Rui Han | 3 | 74 | 11.51 |
| Bogdan Robu | 4 | 14 | 3.41 |
| Sara Bouchenak | 5 | 0 | 0.34 |
| Sonia Ben-Mokhtar | 6 | 3 | 2.42 |
| Lydia Y. Chen | 7 | 432 | 52.24 |