Sustaining the Performance of Artificial Intelligence in Networking Analytics

Date of Award

2023

Degree Name

Ph.D. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Eric Balster

Abstract

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.

Keywords

Networking analytics, Artificial Intelligence, network traffic classification, network security, Internet-of-Things

Rights Statement

Copyright © 2023, Author

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