Name
Abstract | ||
|---|---|---|
While monitoring network traffic at the operator level allows to collect highly valuable data for monitoring large scale distributed attacks, scalability remains a major challenge due to the large data volumes to handle. In particular, collecting and storing NetFlow data is feasible but accurate analysis is still a challenging topic. Hence, this paper leverages an aggregated representation of the network traffic which is further analyzed using dedicated entropic based metrics and machine learning techniques. The main advantage is a reduction of the computational complexity while the accuracy still remains acceptable as highlighted by evaluation on real datasets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CNS.2013.6682763 | Communications and Network Security |
Keywords | Field | DocType |
computational complexity,entropy,learning (artificial intelligence),signal representation,telecommunication security,telecommunication traffic,NetFlow data,aggregated metrics,aggregated representations,computational complexity,entropic based metrics,large scale distributed attacks,machine learning,network traffic,scalable flow analysis,valuable data | Data mining,NetFlow,Computer security,Computer science,Flow (psychology),Computer network,Telecommunication security,Operator (computer programming),Computational complexity theory,Scalability | Conference |
ISSN | Citations | PageRank |
2474-025X | 0 | 0.34 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jerome Francois | 1 | 57 | 4.39 |
| Radu State | 2 | 623 | 86.87 |
| Thomas Engel | 3 | 455 | 42.34 |