Name
Abstract | ||
|---|---|---|
Fraudulent resource consumption (FRC) attacks threaten the economic viability of cloud consumers. Detection of these attacks is difficult as they often blend in with normal traffic patterns although they can still cause dramatic financial consequences. We employ a variety of data science techniques to detect FRC attacks in a cloud environment. Statistical, time series, and machine learning methods all achieve various levels of success and, in some cases, failure at detection. Unfortunately, none of these techniques is independently sufficient for detection for our experiments due to characteristics of the data set we used, but we summarize lessons learned from our research. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CCWC51732.2021.9375938 | 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) |
Keywords | DocType | ISBN |
fraudulent resource consumption (FRC) attacks,time series analysis,machine learning,artificial neural networks | Conference | 978-1-6654-3058-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lauren Courtney | 1 | 0 | 0.34 |
| Xiang Li | 2 | 0 | 0.34 |
| Rongzuo Xu | 3 | 0 | 0.34 |
| Joel Coffman | 4 | 32 | 4.44 |