| Autor: |
Gonzalez, Roberto, Manco, Filipe, Garcia-Duran, Alberto, Mendes, Jose, Huici, Felipe, Niccolini, Saverio, Niepert, Mathias |
| Rok vydání: |
2017 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network. Net2Vec is able to capture data from the network at more than 60Gbps, transform it into meaningful tuples and apply predictions over the tuples in real time. This platform can be used for different purposes ranging from traffic classification to network performance analysis. Finally, we showcase the use of Net2Vec by implementing and testing a solution able to profile network users at line rate using traces coming from a real network. We show that the use of deep learning for this case outperforms the baseline method both in terms of accuracy and performance. |
| Databáze: |
arXiv |
| Externí odkaz: |
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