Open AccessThe research of Random Forest Intrusion Detection Model based on Optimization in Internet of Vehicles
Author(s) -
Yalin Li,
Fēi Li,
Jiaqi Song
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012149
Subject(s) - overfitting , random forest , computer science , intrusion detection system , decision tree , the internet , artificial intelligence , machine learning , network security , constant false alarm rate , decision tree model , data mining , computer security , artificial neural network , world wide web
Security model is the main means to protect the network information security of vehicle. Due to the rapid development of artificial intelligence in recent years, machine learning technology is also emerging in the field of Internet of vehicles security. The random forest model is a strong classifier and can prevent overfitting better than the decision tree model. However, only using the traditional random forest invasion detection model has some problems, such as: the model detection time is long, the false alarm rate is high, the ability of using platform transplantation is poor, etc. In this paper, it is optimized in a lightweight way to reduce the time consumption and improve the accuracy of intrusion detection in the vehicle networking intrusion detection model.