Fei Li1, *, Jiayan Zhang1, Edward Szczerbicki2, Jiaqi Song1, Ruxiang Li 1, Renhong Diao1
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264
Abstract 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 More >
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