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 tuplesin real time. This platform can be used for different purposes ranging from traffic classification to network performance analysis. Finally, we showcasethe 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.
Roberto Gonzalez received his MSc in Telematic Engineering from Polytechnic University of Catalunya and Carlos III University of Madrid and his PhD in Telematic Engineering from Carlos III University of Madrid in 2011 and 2014, respectively. In July 2014, he joined NEC Laboratories Europe where he is currently senior researcher. His research interests are online privacy, social networks and network measurements with a special focus on the data analytics part. He has published more than 20 papers in leading venues in web conferences (WWW, COSN) and networking conferences(TON, IMC, IEEE Networks, Computer Networks, P2P, etc).