A Continuous Authentication Model Based on Improved Flower Pollination Algorithm and Extreme Gradient Boosting

Abstract

The zero trust systems help to improve the overall security of computer networks, while one of the main challenges of zero trust systems is the construction and optimization of continuous authentication models. Aiming at the shortcomings of the existing authentication performance, this paper proposes a continuous authentication model based on an improved flower pollination algorithm (FPA) and extreme gradient boosting (XGBoost) to improve the accuracy of authentication. The model first uses multiple strategies to optimize FPA to obtain MSFPA; then MSFPA is applied to XGBoost for hyper-parameter tuning to obtain MSFPA-XGBoost; and finally, MSFPA-XGBoost is applied to the user's continuous authentication. Among these approaches, MSFPA incorporates chaotic mapping to enhance the initial population. It also utilizes an adaptive transformation probability strategy to dynamically strike a balance between global and local search. Furthermore, it refines the search equation to optimize both global and local search. Additionally, MSFPA employs a pollen cross-boundary correction strategy and incorporates the concept of cross-mutation to augment the algorithm's exploratory capabilities. The experimental results demonstrate that, in the context of parameter optimization tasks, MSFPA exhibits superior performance compared to other optimization algorithms, specifically in terms of search accuracy and convergence speed. In addition, in terms of continuous identity verification effect, compared with each classification model, MSFPA-XGBoost improves the Accuracy, Recall, Precision, F1-Score, and AUC metrics by an average of 1.84%, 2.49%, 2.33%, 2.39%, and 3.11%, which indicates that the proposed model enhances the accuracy of continuous authentication and is more effectively applicable within zero-trust systems.