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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

Abstrakt

The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to verify the accuracy and efficiency of the proposed model, it was evaluated using real vehicle data. The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior. The parameters of the model are self-iteratively updated using the time-based back propagation algorithm. We verified that the model proposed in this study can reach a nearly 96% accurate detection rate.

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Accepted albo Published Version
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Creative Commons: CC-BY otwiera się w nowej karcie

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
CMC-Computers Materials & Continua nr 65, strony 653 - 681,
ISSN: 1546-2218
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Fei L., Jiayan Z., Jiaqi S., Szczerbicki E.: Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks// CMC-Computers Materials & Continua -Vol. 65,iss. 1 (2020), s.653-681
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.32604/cmc.2020.011264
Weryfikacja:
Politechnika Gdańska

wyświetlono 93 razy

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