Net2Vec: Deep Learning for the Network

TL;DR

Net2Vec is a high-performance platform enabling real-time deep learning applications in communication networks, capable of processing over 60Gbps and improving tasks like traffic classification and user profiling.

Contribution

The paper introduces Net2Vec, a flexible platform that integrates deep learning into network operations with real-time processing capabilities and demonstrates its effectiveness in user profiling.

Findings

Net2Vec processes data at over 60Gbps in real time.

Deep learning outperforms baseline methods in network user profiling.

Platform is versatile for various network analysis tasks.

Abstract

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.