基于大数据分析的电力信息系统安全状态监测技术研究

信息系统作为电力物联网建设的核心关键部分,一旦电力信息系统受到威胁,将影响整个电网的安全稳定运行.目前基于电力信息系统的安全防护策略主要集中于传统的保护和检测方法上.然而,许多威胁发生在很短的时间内,很容易被忽略,无法及时发现.这些威胁通常会对电力信息系统造成巨大影响,干扰其正常运行.针对这一问题,文中提出了一种基于大数据分析的电力信息系统安全状态监测机制.基于模糊聚类,有效评估网络运行情况.同时提出了一种博弈理论和机器学习相结合网态势感知模型,有效降低电力信息系统的安全运维风险.仿真结果验证了所提安全状态分析策略的有效性和实用性.

Research on Security State Monitoring Technology of Power Information System Based on Big Data Analysis

The information system is a key part of the construction of the power Internet of Things. Once the power information system is in danger, it will affect the safe and stable operation of the entire power grid. The current security protection strategies based on power information systems mainly focus on traditional protection and detection methods. However, many threats occur in a short period of time and are easily overlooked and cannot be detected in time. These threats usually have a huge impact on the power information system and interfere with its normal operation. In response to this problem, this paper proposes a power information system security status monitoring mechanism based on big data analysis. It will be based on fuzzy clustering to effectively evaluate the network operation. At the same time, a network situation awareness model that combines game theory and machine learning is proposed to effectively reduce the security operation and maintenance risks of power information systems. The simulation results show the effectiveness and practicability of the security state analysis strategy proposed in this paper.