Name
Abstract | ||
|---|---|---|
Novel cybersecurity solutions tend to adopt new mechanisms from emerging fields in order to confront zero-day attacks and unknown signature threats. Deep learning techniques have attracted the interest of the cybersecurity domain, as they offer the flexibility to be trained for various objects and targets, amongst them network anomaly detection. Traditional network anomaly detection methods rely o... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/FMEC54266.2021.9732556 | 2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC) |
Keywords | DocType | ISBN |
Deep Learning,Network Anomaly Detection,NFV,Network Security,CNN | Conference | 978-1-6654-5870-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michail-Alexandros Kourtis | 1 | 0 | 0.68 |
| Andreas Oikonomakis | 2 | 2 | 0.71 |
| Dimitris Papadopoulos | 3 | 2 | 1.11 |
| George Xylouris | 4 | 0 | 0.34 |
| Ioannis P. Chochliouros | 5 | 0 | 0.34 |