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Abstract Header

Sustaining the Performance of Artificial Intelligence in Networking Analytics

Zhang, Jielun
http://orcid.org/0000-0002-2113-2104
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689787494979064

Abstract Details

2023, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical Engineering.
Emerging Artificial Intelligence (AI) techniques, including both Machine Learning algorithms and Deep Learning models, have become viable solutions to support network measurement and management. As the fundamental of network analytics, network traffic classification has recently been studied with the adoption of AI techniques. For example, widely studied AI-based traffic classifiers, developed based on artificial neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), have demonstrated high classification accuracy. However, their performance is limited to the coverage of the knowledge databases, which restricts their effectiveness in dealing with updated or new network applications. To address the limitations, model update mechanisms are introduced, which allow AI-based traffic classification models to sustain high performance by creating a new knowledge base. These mechanisms enable the AI-based network traffic classification models to adapt to those evolving network applications in dynamic network environments. Additionally, the dissertation discusses the challenges of AI performance in network security and resolves them by leveraging the proposed mechanisms.
Eric Balster (Committee Chair)
Hui Wang (Committee Member)
Brad Ratliff (Committee Member)
Feng Ye (Committee Member)
133 p.
Computer Engineering; Electrical Engineering
Networking analytics; Artificial Intelligence; network traffic classification; network security; Internet-of-Things

Recommended Citations

Citations

  • Zhang, J. (2023). Sustaining the Performance of Artificial Intelligence in Networking Analytics [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689787494979064

    APA Style (7th edition)

  • Zhang, Jielun. Sustaining the Performance of Artificial Intelligence in Networking Analytics. 2023. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689787494979064.

    MLA Style (8th edition)

  • Zhang, Jielun. "Sustaining the Performance of Artificial Intelligence in Networking Analytics." Doctoral dissertation, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689787494979064

    Chicago Manual of Style (17th edition)

dayton1689787494979064
134
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.