Research on multi-dimensional security collaborative protection of smart grid integrating adaptive regulation and advanced mimicry defense

Abstract Smart grids are becoming increasingly digital and interconnected, making them more susceptible to numerous cyber-physical threats such as coordinated attacks, zero-day vulnerabilities, and advanced persistent threats. Conventional detection methods that are rule-based, or one-dimensional, are rigid and unable to identify sophisticated, evolving attacks in real-time, and this creates an enormous threat to the stability and security of the grid. To alleviate these challenges, we propose a framework called Multi-dimensional Security Collaborative Protection (MSCP) that optimizes and adapts regulation and integrates a deep mimicry defence to build a more robust smart grid security management system. The aim is to create a global dynamic deception-based defence using intelligent decision-making and self-configuring systems. Operational data, such as voltage level, frequency signals, communications, and access behaviours, are recorded. The data is cleaned, normalized, and processed using a wavelet transform for features to be extracted in the temporal, spatial, and frequency domains. Threat perception is that which takes place using an intelligent Gated Recurrent Unit (GRU-based bi-directional gated recurrent unit (IntGO-BiGRU) method, combining Bidirectional Gated Recurrent Unit (Bi-GRU) for temporal sequences - optimized using IntGO. Risk evaluation is conducted utilizing an adaptive neuro-fuzzy inference system (ANFIS) to quantify the severity of threats and uncertainty. Adaptive Regulation uses an MDP-based Policy Reconfiguration Engine, as it is essential to dynamically reconfigure security policies and modify system responses in real-time. Similarly, using Mimicry Defense through MTD methodologies, including IP mutation, service port rotation, and environment diversification, prevents the enemy from developing predictability and significantly reduces the attack surface. Using the Python MSCP framework, we achieved higher anomaly detection accuracy (97.72%) with reduced false positives (FP). Overall, this research shows that MDP and DL fuzzy reasoning and adaptive decision-making, along with mimicry, help increase the smart grid defense in complex and dynamic operational contexts.