Data security interactive access control for the entire power grid process based on improved ASE algorithm

This paper proposes a secure interactive access control method for the entire process of power grid data based on adaptive integrated online extreme learning machine algorithm. This method integrates online extreme learning machines to detect and recognize abnormal behaviors in the process of power grid data interaction, effectively ensuring the secure interaction of power grid data. The application of adaptive integration strategy further improves the accuracy and robustness of the algorithm. By real-time learning and prediction of power grid data, a dynamic behavior model of power grid data is constructed, which enables real-time monitoring and anomaly detection of data interaction behavior and adapts to the data security requirements in different power grid environments. The experimental results show that this method has significant advantages in the access control of secure data exchange in the entire power grid process. It not only improves the security of power grid data exchange, but also enhances the stability and reliability of the power grid system, providing strong guarantees for the safe and stable operation of the power grid.