Abstract
This paper aims at the low detection accuracy and slow detection speed of traditional machine learning methods in processing large-scale intrusion data. Based on the in-depth analysis and research of deep learning models and intrusion detection methods, this paper proposes hybrid intrusion detection based on deep learning Model DBN-PBT-TSVM. This article focuses on the application of IDS in VANET. First, introduce the characteristics and development of IDS in VANET; analyze the advantages and disadvantages of the existing IDS algorithms in the industry; propose new IDS and apply it to VANET. This paper proposes a Hidden Markov Model (HMM) filter model suitable for IDS under VANET to reduce the load and detection time without compromising the accuracy of detection. The filter model predicts the future behavior (normal or abnormal) of neighboring vehicles (NVs) to quickly filter messages from these vehicles. Experimental research shows that the intersection point Tâ=â95 is taken as the best point. The filter model proposed in this paper can greatly improve the detection time and load without affecting the detection accuracy.
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Acknowledgements
Fund Project 1: This is the phased research result of the âResearch on Security Mechanism and Key Technology Application of Internet of Vehiclesâ (Project No: NY-2020KYYB-08) from Guangzhou Nan yang Polytechnic College.
Fund Project 2: This paper is the mid-stage research result of the project of âBig Data and Intelligent Computing Innovation Research Team (NY-2019CQTD-02)â from Guangzhou Nan yang Polytechnic College.
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Wang, R. (2021). Intrusion Detection Technology of Internet of Vehicles Based on Deep Learning. In: Huang, C., Chan, YW., Yen, N. (eds) 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems. Advances in Intelligent Systems and Computing, vol 1379 . Springer, Singapore. https://doi.org/10.1007/978-981-16-1726-3_40
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DOI: https://doi.org/10.1007/978-981-16-1726-3_40
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