The research of Random Forest Intrusion Detection Model based on Optimization in Internet of Vehicles

Abstract

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.