Security protection strategy of power terminal based on deep learning

被引:0
|
作者
Cai, RongYan [1 ]
Cheng, Yong [1 ]
Jia, YongLiang [2 ]
机构
[1] State Grid Fujian Elect Power Co Ltd, 257 Wusi Rd, Fuzhou 350000, Fujian, Peoples R China
[2] State Grid Hebei Mkt Serv Ctr, Shijiazhuang, Hebei, Peoples R China
来源
2022 6TH INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND INTELLIGENT CONTROL, ISCSIC | 2022年
关键词
Deep learning; Long short-term memory neural network; Security protection; Power terminal; MODEL;
D O I
10.1109/ISCSIC57216.2022.00069
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to solve the problem of security vulnerabilities in embedded power terminals, this paper studies the application of long short-term memory neural network to the security protection of power terminals, and establishes a security protection model. According to the characteristic that the protection model lacks negative samples for training in the training process, the study uses the characteristics of long short-term memory neural network to improve the model, and compares and analyzes it with the same type of model in practical applications. The results show that the model designed by the research has a model accuracy of 0.99 in the original dataset and a model accuracy of 0.98 in the mixed dataset, and the model performance data is the best among the same type of models. It can be seen that the model of research design has sufficient practicability and can provide a new idea for related fields.
引用
收藏
页码:301 / 305
页数:5
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