Open AccessResearch on classification method of new energy vehicle information security risk assessment
Author(s) -
Tonghong Chong,
Xianfeng Jia,
Zhi Wu,
Hao Zhao,
Tianyu Li
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/827/1/012010
Subject(s) - cluster analysis , fuzzy logic , valuation (finance) , risk assessment , information security , computer security , computer science , the internet , network security , fuzzy clustering , hotspot (geology) , risk analysis (engineering) , business , artificial intelligence , finance , geophysics , world wide web , geology
With the rapid development and popularization of intelligent Internet connected vehicle, the automobile and the physical world are combined together, which makes users have strong technology experience and life convenience. While enjoying the convenience and experience brought by the intelligent Internet connected vehicle, we are also faced with the risk of information security in the network world, even the security problems of the network world directly affect the security of the physical world. With the improvement of automobile ‘four modernizations’, the research on information security has become an important topic and research hotspot in the field of automobile. Risk assessment is one of the important research and work contents of automobile information security. Impact valuation is an important indicator of risk assessment. It is very important to determine the optimal cluster number for risk assessment model and risk assessment. In this paper, the fuzzy clustering impact estimates are divided into 2-12 categories during Evergrande’s risk assessment of an electric vehicle. The optimal number of clusters is determined by calculating the mixed F-statistics of each type of data. Conclusion shows that the best clustering number of impact estimates is 5.