@Article{cmc.2025.058802,
AUTHOR = {Feng Wei, Zhao Chen, Yin Wang, Dongqing Liu, Xun Zhang, Zhao Zhou},
TITLE = {Real-Time Identity Authentication Scheme Based on Dynamic Credentials for Power AIGC System},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {82},
YEAR = {2025},
NUMBER = {3},
PAGES = {5325--5341},
URL = {http://www.techscience.com/cmc/v82n3/59882},
ISSN = {1546-2226},
ABSTRACT = {The integration of artificial intelligence (AI) with advanced power technologies is transforming energy system management, particularly through real-time data monitoring and intelligent decision-making driven by Artificial Intelligence Generated Content (AIGC). However, the openness of power system channels and the resource-constrained nature of power sensors have led to new challenges for the secure transmission of power data and decision instructions. Although traditional public key cryptographic primitives can offer high security, the substantial key management and computational overhead associated with these primitives make them unsuitable for power systems. To ensure the real-time and security of power data and command transmission, we propose a lightweight identity authentication scheme tailored for power AIGC systems. The scheme utilizes lightweight symmetric encryption algorithms, minimizing the resource overhead on power sensors. Additionally, it incorporates a dynamic credential update mechanism, which can realize the rotation and update of temporary credentials to ensure anonymity and security. We rigorously validate the security of the scheme using the Real-or-Random (ROR) model and AVISPA simulation, and the results show that our scheme can resist various active and passive attacks. Finally, performance comparisons and NS3 simulation results demonstrate that our proposed scheme offers enhanced security features with lower overhead, making it more suitable for power AIGC systems compared to existing solutions.},
DOI = {10.32604/cmc.2025.058802}
}