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...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.展开更多
基金This work was supported by Research on the Influences of Network Security Threat Intelligence on Sichuan Government and Enterprises and the Development Countermeasure(Project ID 2018ZR0220)Research on Key Technologies of Network Security Protection in Intelligent Vehicle Based on(Project ID 2018JY0510)+3 种基金the Research on Abnormal Behavior Detection Technology of Automotive CAN Bus Based on Information Entropy(Project ID 2018Z105)the Research on the Training Mechanism of Driverless Network Safety Talents for Sichuan Auto Industry Based on Industry-University Synergy(Project ID 18RKX0667),Research and implementation of traffic cooperative perception and traffic signal optimization of main road(Project ID 2018YF0500707SN)Research and implementation of intelligent traffic control and monitoring system(Project ID 2019YGG0201)Remote upgrade system of intelligent vehicle software(Project ID 2018GZDZX0011).
文摘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.
