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Dynamic Security Perception Method for Power Production Data Network Based on PPDR
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DOI：10.1007/978-981-95-1103-7_19.png)
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Abstract
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With the rapid digital transformation of the power industry, the security of power production data networks is crucial for the stable operation of power systems and national energy security. However, these networks face increasing cybersecurity threats such as hacking, malware, and data breaches. This paper proposes a Policy-Protection-Detection-Response (PPDR)-based dynamic security awareness method for power production data networks. By integrating security access control policies, encryption technologies, AI-driven detection, and response systems, the method achieves full-chain dynamic security awareness. Combined with machine learning algorithms, it monitors, analyzes, and standardizes network status and threat responses, providing comprehensive threat perception capabilities. This study offers new insights for enhancing the security and stability of power data networks and advances the field of dynamic security awareness.
Keywords:
Power Production Data Network
Dynamic Security Awareness
PPDR
Machine learning algorithms
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