Open AccessInformation Hiding Technology under the Background of Power System Network Security
Author(s) -
Jie Huang,
Jing Li,
Jie Wang,
Li Tian
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1852/2/022064
Subject(s) - computer science , information hiding , network security , computer security , information security , electric power system , robustness (evolution) , information technology , algorithm , power (physics) , artificial intelligence , image (mathematics) , biochemistry , physics , chemistry , quantum mechanics , gene , operating system
Power grid plays an important role in the development of the country and people’s life. It can be said that both production and life are inseparable from the power grid. Therefore, when the power system network security threats, the whole life will be adversely affected. Traditional power system network security protection measures have been unable to meet the current demand of power system network security protection. Information hiding technology is an advanced network information hiding technology, which has been widely used in recent years. Therefore, this paper puts forward the research of information hiding technology under the background of power system network security. In this paper, the power system network and information hiding technology are deeply studied. The analysis shows that the latest information hiding technology can play a good role in security protection of power system network, especially for various camouflage attacks. In this paper, according to the actual security requirements of power system network, combined with the main characteristics of information hiding technology, the traditional information hiding algorithm is optimized and improved. In the verification experiment, compared with the traditional algorithm, the NC value of the optimization algorithm in this paper has been significantly improved. The NC value of the traditional algorithm of 5x5 Gaussian low-pass filters is 0.619, while the algorithm of this paper reaches 0.869, the effect is obviously improved. Analysis shows that the algorithm in this paper further improves the NC value of the traditional algorithm, and improves the robustness while ensuring the image quality.