Application-based network traffic generator for networking ai model development

Date of Award


Degree Name

M.S. in Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Feng Ye


The growing demands for communication and complex network infrastructure relay on overcoming the network measurement and management challenges. Lately, artificial intelligence (AI) algorithms have considered to improve the network system, e.g., AI-based network traffic classification, traffic prediction, intrusion detection system, etc. Most of the development of networking AI models require abundant traffic data samples to have a proper measuring or managing. However, such databases are rare to be found publicly. To counter this issue, we develop a real-time network traffic generator to be used by network AI models. This network traffic generator has a data enabler that reads data from real applications and establishes packet payload database and a traffic pattern database. The packet payload database has the data packets of real application, where network traffic generator locates the payload in the capture file (PCAP). The other database is traffic pattern database that contains the traffic patterns of a real application. This database depends on the timestamp in each packet and the number of packets in the traffic sample to form a traffic database. The network traffic generator has a built-in network simulator that allows to mimic the real application network traffic flows using these databases to simulate the real-traffic application. The simulator provides a configurable network environment as an interface. To assess our work, we tested the network traffic generator on two network AI models based on simulated traffic, i.e., AI classification model, and AI traffic prediction. The simulation performance and the evaluation result showed improvement in networking AI models using the proposed network traffic generator, which reduce time consuming and data efficiency challenges.


Artificial Intelligence, Communication, Computer Engineering, Computer Science, Educational Software, Educational Technology, Electrical Engineering, Information Science, Information Systems, Information Technology, Systems Science, Technical Communication, Technology, NS-3, Artificial Intelligent network traffic, traffic generator, traffic simulator, LSTM, AI classification, PCAP, traffic flow, packets, payload database, traffic database, AI network dataset, AI-based network, networking AI models, AI algorithm, Finding highest peaks

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Copyright © 2021, author.