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

被引:8
作者
Wang, Xiaojie [1 ]
Nie, Laisen [2 ]
Ning, Zhaolong [1 ]
Guo, Lei [1 ]
Wang, Guoyin [3 ]
Gao, Xinbo [4 ]
Kumar, Neeraj [5 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Inst Intelligent Commun & Network Secur, 2 Chongwen Rd, Chongqing 400065, Peoples R China
[2] Northwestern Polytech Univ, Sch Elect & Informat, 1 Dongxiang Rd, Xian 710072, Peoples R China
[3] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, 2 Chongwen Rd, Chongqing 400065, Peoples R China
[4] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Image Cognit, 2 Chongwen Rd, Chongqing 400065, Peoples R China
[5] Thapar Inst Engn & Technol, Dept Comp Sci & Engn, Bhadson Rd, Patiala, Punjab, India
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Internet of vehicles; traffic prediction; network security; deep learning;
D O I
10.1145/3433548
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:20
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