Research on evaluation model of health state information security protection for automotive braking system based on machine learning

The Long Short-Term Gradient (LSTG) architecture innovatively combines the time series analysis capability of Long short-term memory network (LSTM) with the efficient training strategy of Mini-batch Gradient Descent (MGD). It has brought innovation to the research of health state information security protection evaluation model of automobile brake system based on machine learning. LSTG uses LSTM to capture the time-dependent characteristics of sensor data in automotive braking system, accelerates model iteration through MGD strategy, and significantly improves the training efficiency and evaluation accuracy of the model. Through experimental comparison, LSTG not only greatly enhances the accuracy rate, recall rate and accuracy rate of the model under complex environment, but also proves the reliability and feasibility of the model in the automotive braking system.

I. Introduction

With the development of autonomous driving technology and intelligent connected vehicles, the importance of vehicle safety and information security has become increasingly prominent. Especially in the automotive braking system, any potential failure or safety hazards may pose a serious threat to driving safety. Thus, developing an effective method for real-time monitoring and evaluation of the health status of automobile braking systems is of great importance. Traditional rule-based methods and simple statistical analysis have been difficult to meet the complexity and safety requirements of modern automotive braking systems. Therefore, this paper proposes an innovative deep learning framework, Long Short-Term Gradient (LSTG). It combines Long Short-Term Memory (LSTM) with the Mini-batch Gradient Descent (MGD) strategy, significantly enhancing the performance of health state information security protection for automotive braking systems based on machine learning.