Name
Abstract | ||
|---|---|---|
The storage of large amounts of network data is a challenging problem, in particular if it still needs to be actively consulted as for example in the case of network forensics. Here we propose a method to compress NetFlow data while simultaneously adding domain knowledge. Our method is based on a pattern classification scheme by considering all flows from a single source IP address simultaneously. Each pattern can be described by at most 19 attributes that give a good statistical description of the original NetFlow data, while minimising information loss. We estimate that on average a factor of about 300 in storage space can be gained. The process is explained using a real world dataset from a large, high-speed, network, and a formal rationale is provided. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BigDataSecurity-HPSC-IDS.2016.69 | 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS) |
Keywords | DocType | Citations |
pattern classification,data summarisation,compression,storage,netflow | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rémon Cornelisse | 1 | 0 | 0.34 |
| Mortaza S. Bargh | 2 | 199 | 21.12 |
| Sunil Choenni | 3 | 309 | 111.82 |
| Debora Moolenaar | 4 | 0 | 0.34 |
| Luc V. De Zeeuw | 5 | 0 | 0.34 |