Improved Trust Evaluation Model Based on PBFT and Zero Trust Integrated Power Network Security Defense Method
Year: 2025
Type: article
Abstract: In traditional power networks, security protection models primarily rely on perimeter-based defenses, utilizing firewalls, virtual private networks (VPNs), and identity authentication to block external threats. However, once a node within the power system is compromised, attackers can exploit it as a pivot to launch lateral movement attacks from within the system, posing serious threats to the core operations of the power grid. To address the increasingly complex cybersecurity landscape, this paper proposes a security defense approach that integrates an improved trust evaluation model based on the Practical Byzantine Fault Tolerance (PBFT) algorithm with a zero-trust architecture, leveraging the structural and functional symmetry among network nodes. The PBFT algorithm’s fault tolerance and consensus mechanisms are leveraged to ensure dynamic trust scoring across multiple nodes. This approach guarantees that each node has an equal role in the system’s operations, maintaining fairness and security across the network. Furthermore, the primary node in the PBFT consensus process is redefined as the arbitration node in the zero-trust framework, and faulty nodes can be automatically replaced through the view change protocol, thereby mitigating the centralization risk inherent in traditional zero-trust models. Experimental results demonstrate that the proposed approach achieves high accuracy and robustness in defending against both internal and external attacks in power network scenarios, highlighting the role of symmetry in enhancing secure and balanced system operations.
Source: Symmetry
DOI: 10.3390/sym17111982
Authors: Xiaoyun Liao, Sen Yang, Jun Xu, Li Liu, Wei Liang, Shengjie Yu, Yimu Ji, Shangdong Liu
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G7681676954
OpenAlex ID: W4416296517
