Improvement on a Traffic Data Generator for Networking AI Algorithm Development

Recently, many Artificial Intelligence (AI) based schemes have been proposed to support network measurement and management, such as network traffic classification, intrusion detection, traffic prediction, etc. These AI schemes have demonstrated promising performance in supporting networking. However, the development of these AI schemes requires a massive amount of fresh databases. The scarcity and futility of public datasets are straining the development of the networking AI models. Not to mention that most available datasets are not up-to-date. Collecting new datasets can be time-consuming and restricted by networking capabilities. To address the issues, we have introduced a real-application enabled network traffic generator. In this work, we further enhance the network traffic generator with more functionalities. In particular, a traffic flow segment scheme is proposed for the quick establishment of traffic flow databases. An intelligent generator is implemented to simulate point-to-point communications, including network multiplexing, and network duplexing. The evaluation results demonstrate that the improved intelligent traffic generator can generate a large amount of diverse network traffic with practical settings more efficiently than collecting data in real life. Moreover, a case study is given to demonstrate the quality of the generated traffic data.