Adelaide Research & Scholarship
Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/111877
| Citations | ||
| Scopus | Web of Science® | Altmetric |
|---|---|---|
|
?
|
?
|
|
| Type: | Journal article |
| Title: | A BasisEvolution framework for network traffic anomaly detection |
| Author: | Xia, H. Fang, B. Roughan, M. Cho, K. Tune, P. |
| Citation: | Computer Networks, 2018; 135:15-31 |
| Publisher: | Elsevier |
| Issue Date: | 2018 |
| ISSN: | 1389-1286 1872-7069 |
| Statement of Responsibility: | Hui Xia, Bin Fang, Matthew Roughan, Kenjiro Cho, Paul Tune |
| Abstract: | Abstract not available |
| Keywords: | Anomaly detection; basis; evolution; low false-alarm probability; SVD |
| Rights: | ©2018 Elsevier B.V. All rights reserved |
| DOI: | 10.1016/j.comnet.2018.01.025 |
| Grant ID: | http://purl.org/au-research/grants/arc/DP110103505 http://purl.org/au-research/grants/arc/CE140100049 |
| Published version: | http://dx.doi.org/10.1016/j.comnet.2018.01.025 |
| Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.