Network Traffic Classification Using Distributed ML-Based Data Parallelization Approach

The large datasets related to network traffic flow classification in the internet benefit machine learning (ML) and deep learning (DL) models for more accurate classification, which is used in many applications such as in detecting traffic anomaly for prevention of potential cyber-attacks. Data Parallelization allows for faster training times on large datasets as shown in our results, and it is also beneficial in the cloud-edge environment by allowing efficient distribution of computation and data across multiple nodes. We deployed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), advanced hybrid Convolutional LSTM (ConvLSTM), Convolutional GRU (ConvGRU), and XGBoost algorithm using Data Parallelization approach. The experimental setup was implemented in the cloud and parallel training was executed using Nvidia Tesla Graphics Processing Units (GPUs). Lastly comparison of the performance metric results is presented between the non-parallel centralized (single node) and data parallel distributed approaches.