Net2Vec: Deep Learning for the Network
This work addresses network management challenges for operators by enabling real-time deep learning applications, though it appears incremental as it adapts existing methods to a new domain.
The authors tackled the problem of applying deep learning to network data by introducing Net2Vec, a platform that captures data at over 60Gbps and processes it in real time, showing it outperforms baseline methods in accuracy and performance for tasks like user profiling.
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.