| Title | Abnormal Bus Data Detection of Intelligent and Connected Vehicle Based on Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Dong, C., Liu, Y., Zhang, Y., Shi, P., Shao, X., Ma, C. |
| Conference Name | 2018 IEEE International Conference on Computational Science and Engineering (CSE) |
| Keywords | abnormal bus data, abnormal bus data analysis, abnormal bus data detection, Attack, Automotive engineering, compositionality, connected vehicles, Data analysis, data centers, detection, Floods, Intelligent Data and Security, Intelligent Data Security, intelligent transportation systems, Intelligent vehicles, intrusion detection system, Logic gates, neural nets, Neural Network, optimized neural network, pubcrawl, Resiliency, Scalability, security of data, Standards, Vulnerability |
| Abstract | In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%. |
| DOI | 10.1109/CSE.2018.00031 |
| Citation Key | dong_abnormal_2018 |
Abnormal Bus Data Detection of Intelligent and Connected Vehicle Based on Neural Network