Genetic Algorithm-Based Power System Information Security Risk Assessment Method

Abstract

With the innovation and application of new-generation information technologies such as cloud computing, Internet of Things, and mobile Internet, the importance of information security issues has become more prominent. As an important industry linking people’s livelihood, the electric power industry will affect the safe operation of electric power once an information security incident occurs, and even lead to a large-scale blackout. Therefore, studying the information security risk assessment model of electric power enterprises has important theoretical value and practical significance. The purpose of this paper is to study the method of power system information security risk assessment based on genetic algorithm. This paper establishes a security risk quantification model, determines the operating parameters of the genetic algorithm in specific operations based on some literature cases, and compares the results in the literature with those in this paper. Based on a large number of investigations and interviews, this paper conducts research on information security risks, comprehensively considers the threats and vulnerabilities of the system, and analyzes the important impact of information security risks from the internal management, personnel, technology, and systems of electric power enterprises. Factors, the establishment of a power enterprise information security risk evaluation index system, this paper obtains data from actual system evaluation cases, uses multiplication, matrix method, and the genetic algorithm proposed in this paper to calculate the risk quantification model calculation results, and verifies the genetic algorithm the rationality and feasibility of combining with risk assessment, this method has practical application value. Experimental research shows that the risk value calculated by the genetic algorithm based on the risk quantification model proposed in this paper is more than 70% similar to the results calculated by the multiplication method and the matrix method, which proves that the model and calculation method established in this paper can be used. Applied to the specific evaluation implementation process.