A brain–edge co-evolution framework for zero-trust real-time hot patching in power equipment

Within zero-trust security frameworks, real-time hot patching presents numerous obstacles, such as excessive authentication delays, which contradict the millisecond control requirement, no quantitative approaches to mitigate runtime structural disorder during patching, and the lack of operational human experience integrated with automated decision-making systems. These challenges make most automated, zero-trust patching systems unworkable for fault intolerant systems. We present a brain-edge co-evolution framework for zero-trust real-time hot patching. This framework uses human EEG signals-derived risk intuition as a component of the security decision-making process, thereby improving security from a human-centric perspective. Additionally, this framework uses a multidimensional negative entropy model to quantify and regulate the system’s level of structural order, thus

preventing structural disorder during runtime updates. This approach attempts to meet the most extreme security verification and real-time constraints through millisecond-level eBPF-based atomic code replacement. The framework has been validated experimentally on a Real-Time Digital Simulation (RTDS) simulation platform and on a real Intelligent Electronic Device (IED) cluster, and demonstrates the ability to reduce the latency of high-risk security decisions and increase system stability during critical updates, addressing the most pressing issues of contemporary systems.

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