Comparison of HMM and RNN models for network traffic modeling

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.

Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

“…Generally, the RNN model is used to generate discrete sequences and predict continuous variables [31]. The schematic diagram of the RNN method is shown in Figure 7.…”