Internet of Things security posture prediction based on ADE ABiGRU
Peng Xingwei 1，Yuan Lingyun 1,2
1 College of Information Science and Technology, Yunnan Normal University, Kunming 650500, China;2 Key Laboratory of Educational Information for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, China
Abstract： Addressing the complexity and variability in IoT security situation prediction, this paper proposes an ADEABiGRUbased IoT security posture prediction model. The model merges bidirectional gated recurrent units, multihead attention mechanisms, and residual structures, optimized through adaptive differential evolution to enhance its ability to capture complex temporal dependencies and analyze data across multiple dimensions. Refinement of the adaptive mechanism within the adaptive differential evolution algorithm ensures thorough consideration of temporal data characteristics, improving global search efficiency and local approximation accuracy. Experimental results on the ToN_IoT dataset show that the model outperforms traditional algorithms in terms of MAPE, R2, and MSE, demonstrating higher predictive accuracy and stability.
