Feasibility of State Space Models for Network Traffic Generation

Author:

Chu Andrew1ORCID,Jiang Xi1ORCID,Liu Shinan1ORCID,Bhagoji Arjun1ORCID,Bronzino Francesco2ORCID,Schmitt Paul3ORCID,Feamster Nick1ORCID

Affiliation:

1. University of Chicago

2. École Normale Supérieure de Lyon

3. University of Hawai'i at Mānoa

Funder

ANR

NSF (National Science Foundation)

Publisher

ACM

Reference60 articles.

1. Sebastian Abt and Harald Baier. 2014. Are we missing labels? A study of the availability of ground-truth in network security research. In 2014 third international workshop on building analysis datasets and gathering experience returns for security (badgers). IEEE 40--55.

2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.0473

3. A tool for the generation of realistic network workload for emerging networking scenarios

4. Inferring Streaming Video Quality from Encrypted Traffic

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