基于网络物理协同的网络化直流微电网分层传感器欺骗防御框架
Hierarchical Sensor-Spoofing Defence Framework for Networked DC Microgrids via Cyber-Physical Coordination
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中文总结 AI 辅助
针对网络化直流微电网传感器欺骗防御研究不足，提出基于网络物理协同的分层框架，通过MG-DER协同检测与缓解，经试验台实验验证其在多攻击场景下有效。
中文摘要 AI 辅助
恶意访问远程监控与控制系统操纵分布式能源资源（DERs）参考点的网络攻击，与物理域中电压/电流传感器受电磁干扰（EMI）的脆弱性问题并行存在。现有针对传感器欺骗攻击的研究可分为物理防护与网络检测/缓解两类，这些防御方法在单个DER中平衡成本、安全性与性能时各有优劣，但针对跨DER的多层高效协同的系统研究仍有限。为此，本文提出一种分层框架，通过多层网络物理协同检测并缓解网络化微电网（NMGs）中的传感器欺骗攻击。该框架仅需在微电网（MGs）的公共耦合点（PCC）等关键节点部署物理防护技术，即可基于受保护的传感器读数采用网络检测/缓解算法，以应对DER中的传感器欺骗攻击。该框架采用MG-DER协同主动检测方案，通过策略性触发参数扰动，可有效揭露智能传感器欺骗攻击；之后激活基于MG-DER协同的缓解方案，递归且准确地估计传感器偏差。在网络物理直流NMG试验台上开展的实验，证实了该框架在各类攻击场景下的有效性。
英文摘要
In parallel to the cyber attack that manipulates the reference points of distributed energy resources (DERs) by maliciously accessing the remote monitoring and control system, the vulnerability of voltage/current sensors to electromagnetic interference (EMI) in the physical domain has been widely discussed. Existing research efforts against sensor spoofing attacks can be classified into physical prevention and cyber detection/mitigation. These defence methods each have strengths and weaknesses in balancing cost, security, and performance in a single DER, yet systematic research on their multi-layer efficient coordination across DERs remains limited. Towards this end, this paper proposes a hierarchical framework to detect and mitigate sensor spoofing attacks in networked microgrids (NMGs) via {multi-layer cyber-physical coordination}. It requires only to deploy physical prevention technologies at critical points, i.e., the local points of common coupling (PCC) of MGs, such that cyber detection/mitigation algorithms can be adopted based on the secured sensor readings to counter sensor spoofing attacks in DERs. The framework employs an MG-DER coordinated proactive detection scheme to strategically trigger parameter perturbations, under which the intelligent sensor spoofing attacks can be {effectively} disclosed. Afterwards, mitigation schemes based on MG-DER coordination are activated to recursively and accurately estimate sensor biases. Experiments on a cyber-physical DC NMG testbed confirm the framework's effectiveness across diverse attack scenarios.
