Guo, Y.; Wang, Y.; Khan, F.; Al-Atawi, A.A.; Abdulwahid, A.A.; Lee, Y.; Marapelli, B. Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration. Sensors2023, 23, 7091.
Guo, Y.; Wang, Y.; Khan, F.; Al-Atawi, A.A.; Abdulwahid, A.A.; Lee, Y.; Marapelli, B. Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration. Sensors 2023, 23, 7091.
Guo, Y.; Wang, Y.; Khan, F.; Al-Atawi, A.A.; Abdulwahid, A.A.; Lee, Y.; Marapelli, B. Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration. Sensors2023, 23, 7091.
Guo, Y.; Wang, Y.; Khan, F.; Al-Atawi, A.A.; Abdulwahid, A.A.; Lee, Y.; Marapelli, B. Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration. Sensors 2023, 23, 7091.
Abstract
Traffic shaping 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 shaping 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 shaping 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 shaping 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 shaping 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 shaping in SDNs, enabling efficient resource management and QoS assurance
Keywords
Traffic shaping; 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))
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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