Research on intelligent decision-making algorithm and optimization method in information security risk management of power supply enterprises - Zeng, Qian; Gao, Xue; Yuan, Zhongjun; Xiang, Jingye; He, Siyang (2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE), 2025)
Traditional risk management methods relying on manual experience and rules have been difficult to deal with complex and changeable security threats. This paper explores the application of neural network algorithm based on deep learning in information security risk management. The algorithm has the ability of self-learning, self-adaptation and nonlinear mapping by imitating the structure of human brain neural network. The research contents include the selection and implementation of the algorithm, the research and application of the optimization method, and the actual effect evaluation. Through theoretical analysis and practical verification, this paper reveals the potential and advantages of intelligent decision algorithm in improving risk management efficiency and reducing risk loss. The research results show that the neural network algorithm is superior to the rule-based method and simple statistical model in accuracy, recall and F1 score, showing high prediction accuracy and stability. In addition, this paper also established an automatic trigger system and manual intervention mechanism to improve the emergency response efficiency and scientific decision-making. Finally, this study provides a new idea and method of information security risk management for power supply enterprises, which is of great significance to ensure energy security and social stability.