Paper Info
Title
Abnormal Bus Data Detection of Intelligent and Connected Vehicle Based on Neural Network
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%.
Year
DOI
Venue
2018
10.1109/CSE.2018.00031
2018 IEEE International Conference on Computational Science and Engineering (CSE)
Keywords
Field
DocType
abnormal bus data,detection,vulnerability,attack,neural network
Training set,Logic gate,Data detection,Computer science,Computer network,Connected vehicle,Artificial neural network,Intrusion detection system
Conference
ISSN
ISBN
Citations 
1949-0828
978-1-5386-7650-9
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Changqing Dong100.68
Yangyang Liu200.68
Yanan Zhang396.92
peiji shi401.35
Xuebin Shao500.68
Chao Ma68527.49