The shift to smart grids has made electrical power systems more vulnerable to sophisticated cyber threats. To protect these systems, holistic security measures that encompass preventive, detective, and reactive components are required, even with encrypted data. However, traditional intrusion detection methods struggle with encrypted traffic, our research focuses on the low-level communication layers of encrypted power grid systems to identify irregular patterns using statistics and machine learning. Our results indicate that a harmonic security concept based on encrypted traffic and anomaly detection is promising for smart grid security; however, further research is necessary to improve detection accuracy.

翻译：智能电网的转型使电力系统更易遭受复杂网络威胁。为保护这些系统，即使面对加密数据，也需要涵盖预防、检测与响应组件的整体安全措施。然而传统入侵检测方法难以处理加密流量，本研究聚焦于加密电网系统的底层通信层，利用统计学与机器学习识别异常模式。研究结果表明，基于加密流量与异常检测的协同安全理念对智能电网安全具有前景，但需进一步研究以提升检测精度。