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High-risk Problem of Penetration Testing of Power Grid Rainstorm Disaster Artificial Intelligence Prediction System and Its Countermeasures

机译:电网暴雨灾害人工智能预测系统渗透测试高风险问题及对策

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摘要

System penetration testing is an important measure of discovering information system security issues. This paper summarizes and analyzes the high-risk problems found in the penetration testing of the artificial storm prediction system for power grid storm disasters from four aspects: application security, middleware security, host security and network security. In particular, in order to overcome the blindness of PGRDAIPS current SQL injection penetration test, this paper proposes a SQL blind bug based on improved second-order fragmentation reorganization. By modeling the SQL injection attack behavior and comparing the SQL injection vulnerability test in PGRDAIPS, this method can effectively reduce the blindness of SQL injection penetration test and improve its accuracy. With the prevalence of ubiquitous power internet of things, the electric power information system security defense work has to be taken seriously. This paper can not only guide the design, development and maintenance of disaster prediction information systems, but also provide security for the Energy Internet disaster safety and power meteorological service technology support.
机译:系统渗透测试是发现信息系统安全问题的重要措施。本文从应用安全性,中间件安全性,主机安全性和网络安全性四个方面总结和分析了针对电网风暴灾害的人工风暴预测系统的渗透测试中发现的高风险问题。特别是,为了克服PGRDAIPS当前的SQL注入渗透测试的盲目性,本文提出了一种基于改进的二阶碎片重组的SQL盲bug。通过对SQL注入攻击行为进行建模并在PGRDAIPS中比较SQL注入漏洞测试,该方法可以有效地减少SQL注入渗透测试的盲目性并提高其准确性。随着无处不在的电力物联网的普及,电力信息系统的安全防御工作必须予以认真对待。本文不仅可以指导灾害预报信息系统的设计,开发和维护,还可以为能源互联网的灾害安全和电力气象服务技术提供安全保障。

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