Research on Security Audit Technology of Smart Grid Database Based on Neural Networks
Xubin Lin, Situo Zhang, Feifei Hu, Wu Liu · 2023
Smart grid databases store a vast amount of sensitive data and require secure auditing to ensure the protection of the data. Traditional methods of auditing smart grid databases are time-consuming and often rely on manual processing, leading to inefficiencies in the audit process. In this paper, we propose the use of artificial intelligence to address these limitations. Specifically, we introduce a deep convolution neural network for the extraction of smart grid data information and the conduct of data security audits. This method offers several advantages over traditional audit methods. Firstly, it eliminates the need for manual analysis of the data, reducing the time and effort required to perform the audit. Secondly, by feeding the data directly into the model for processing, the proposed method is able to process the data much more quickly and efficiently than traditional methods. Experimental results show that the proposed method is highly effective in conducting smart grid database security audits. The proposed method offers a promising solution for the efficient and effective auditing of smart grid databases, and has the potential to play a significant role in ensuring the security and protection of the critical infrastructure.