Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

Baoshan Xie, Fei Li (), Hao Li, Liya Wang and Aimin Yang
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Baoshan Xie: Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
Fei Li: Shanxi Jianlong Industrial Co., Ltd., Yuncheng 044000, China
Hao Li: Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China
Liya Wang: Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
Aimin Yang: Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2023, vol. 11, issue 16, 1-15

Abstract: In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.

Keywords: Internet of Things; network security situation assessment; feature optimization; sparrow search algorithm; light gradient boosting machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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