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Deep Learning-Based Network Traffic Prediction for Secure Backbone Networks in Internet of Vehicles

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Published:14 November 2022Publication History
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Abstract

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.

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  1. Deep Learning-Based Network Traffic Prediction for Secure Backbone Networks in Internet of Vehicles

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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 22, Issue 4
      November 2022
      642 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3561988
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 14 November 2022
      • Online AM: 3 February 2022
      • Accepted: 2 November 2020
      • Revised: 11 October 2020
      • Received: 8 September 2020
      Published in toit Volume 22, Issue 4

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