Open AccessIntrusion Detection Method of Electric Power Information Network in Cloud Computing Environment
Author(s) -
Jiaqi Zhang,
Guoping Feng,
D. K. Zhou,
Mingjiu Li
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 - 10.1088/1742-6596/2113/1/012050
Subject(s) - cloud computing , computer science , intrusion detection system , electric power , process (computing) , network security , artificial neural network , power grid , intrusion , real time computing , electric power system , power (physics) , data mining , computer security , artificial intelligence , operating system , physics , geochemistry , quantum mechanics , geology
With the widespread application of power grid systems, the information security problems faced by power grids have become more obvious. Various internal and external intrusion attacks that occur frequently have become an important issue affecting the normal operation of power generation and operations. The purpose of this paper is to study the intrusion detection method of electric power information(PI) network in the cloud computing environment. With the help of the cloud platform’s ability to process big data, and based on the analysis of the PI network structure, a DBN optimized BP network algorithm is proposed, and the optimized BP neural network is used as a runtime classification program. Experimental results show that MR-DBN-BP has a detection rate of 96.7% for intrusion detection of PI networks, which can effectively detect intrusions and effectively protect the power dispatch system network.