Net2Vec: Deep Learning for the Network
원문 초록
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.
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MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification
Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification
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