Name
Abstract | ||
|---|---|---|
Network intrusion detection is one of the most import tasks in today's cyber-security defence applications. In the field of unsupervised learning methods, variants of variational autoencoders promise good results. The fact that these methods are very computationally time-consuming is hardly considered in the literature. Therefore, we propose a new two-stage approach combining a fast preprocessing or filtering method with a variational autoencoder using reconstruction probability. We investigate several types of anomaly detection methods mainly based on autoencoders to select a pre-filtering method and to evaluate the performance of our concept on two well established datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.5220/0010233404500457 | ICISSP: PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY |
Keywords | DocType | Citations |
Autoencoder, Deep Learning, Anomaly Detection, Network Intrusion Detection, Variational Autoencoder | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helmut Neuschmied | 1 | 0 | 0.34 |
| Martin Winter | 2 | 0 | 0.34 |
| Katharina Hofer-Schmitz | 3 | 0 | 0.34 |
| Branka Stojanović | 4 | 2 | 1.41 |
| Ulrike Kleb | 5 | 0 | 0.34 |