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Application-Based Network Traffic Generator for Networking AI Model Development

Alsulami, Khalil Ibrahim D

Abstract Details

2021, Master of Science in Computer Engineering, University of Dayton, Electrical and Computer Engineering.
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.
Feng Ye (Committee Chair)
Tarek Taha (Committee Member)
John Loomis (Committee Member)
47 p.

Recommended Citations

Citations

  • Alsulami, K. I. D. (2021). Application-Based Network Traffic Generator for Networking AI Model Development [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619387614152354

    APA Style (7th edition)

  • Alsulami, Khalil. Application-Based Network Traffic Generator for Networking AI Model Development . 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619387614152354.

    MLA Style (8th edition)

  • Alsulami, Khalil. "Application-Based Network Traffic Generator for Networking AI Model Development ." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619387614152354

    Chicago Manual of Style (17th edition)