Name
Abstract | ||
|---|---|---|
Major applications for statistical modeling of network traffic flows can be found in network testing and imitating of unavailable devices. Since packet-level modeling is considered, packet size (PS) and inter-arrival time (IAT) features are sufficient for accurate statistics. Two models are compared based on the hidden Markov model (HMM) framework and a recurrent neural network (RNN). In the RNN model, the feature space is encoded with latent components of a Gaussian mixture model (GMM). The comparison is carried out with a voice Skype call and traffic of an IoT device, and evaluated with the rolling entropy and Kulback-Leibler divergence (KLD) metrics that are derived from the generated PS and IAT parameters. The results show that the RNN is applicable for the packet-level modeling task, but it underperforms the HMM. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/itl2.147 | INTERNET TECHNOLOGY LETTERS |
Keywords | DocType | Volume |
computer networks, hidden Markov model (HMM), recurrent neural networks (RNNs), traffic modeling | Journal | 3 |
Issue | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Radion Bikmukhamedov | 1 | 0 | 0.34 |
| Adel Nadeev | 2 | 1 | 2.04 |
| Guido Maione | 3 | 54 | 12.37 |
| Domenico Striccoli | 4 | 31 | 8.13 |