Open AccessStudy on A Random Forest Improvement Model in Internet of Vehicles
Author(s) -
Yalin Li,
Fēi Li,
Jiayan Zhang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/428/1/012004
Subject(s) - overfitting , random forest , computer science , the internet , decision tree , computer security , nutmeg , artificial intelligence , artificial neural network , world wide web , medicine , traditional medicine
Security model is the main means to protect the information security of automobile network. Among many relevant security models, stochastic forest model is a strong classifier model and can better prevent overfitting than decision tree model. It has good characteristics in resisting flood attacks and other aspects, but it has poor ability to resist Sybil attacks. Therefore, an identity authentication system is added on the basis of the original random forest model, which can resist Sybil attacks while supporting the work in the Internet of vehicles environment. Experimental results show that the model can achieve the described effect.