Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestrationopen access
- Authors
- Guo,Yanmin; Wang,Yu; Khan,Faheem; Al-Atawi,Abdullah A.; Abdulwahid ,Abdulwahid Al; Lee,Youngmoon; Marapelli, Bhaskar
- Issue Date
- Aug-2023
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Keywords
- traffic management; anomaly detection; intrusion detection; network security; internet of things; network traffic analysis; machine learning; SDN (software-defined networking); GNN (graph neural network); MAB (multi-armed bandit)
- Citation
- Sensors, v.23, no.16, pp 7091 - 7091
- Pages
- 1
- Indexed
- SCIE
SCOPUS
- Journal Title
- Sensors
- Volume
- 23
- Number
- 16
- Start Page
- 7091
- End Page
- 7091
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113832
- DOI
- 10.3390/s23167091
- ISSN
- 1424-8220
1424-3210
- Abstract
- Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.
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Collections - COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF ROBOT ENGINEERING > 1. Journal Articles
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