Monitoring safety prediction method and system based on model optimization

Abstract

The invention relates to the field of Internet information processing, and provides a monitoring safety prediction method and system based on a chaotic particle swarm optimization algorithm optimization prediction model, equipment and a medium aiming at the defects of high randomness, large calculation amount, low speed and low efficiency of a parameter adjustment method in an existing data safety monitoring model. A sample data set is divided into a plurality of training subsets and a test set, parameter optimization of a constructed prediction model algorithm is performed by using the plurality of groups of training subsets according to a chaos particle swarm optimization algorithm, and a plurality of groups of optimal parameters are acquired, so that a model with optimized parameters is trained and is utilized to complete prediction of monitoring data. The optimization process is added to the modeling process to achieve parameter optimization, model inaccuracy caused by randomness is avoided, the optimization process is improved in combination with the chaos thought, the parameter optimization effect is improved, the parameter optimization speed is increased to optimize model efficiency, model quality and reliability are guaranteed, and then data safety monitoring accuracy and judgment efficiency are improved.

Description

technical field [0001] The present invention relates to the technical field of data security processing, in particular to the field of big data classification and risk prediction processing, in particular to a method and system for monitoring security prediction based on model optimization. Background technique [0002] In the field of Internet big data and its data security, using integrated models such as XGBoost and LightGBM to predict and evaluate data security is playing an increasingly important role. However, in the process of big data and its security processing, various models are used When , the selection of different parameters often determines the performance of the model. The classic integrated model parameter tuning methods involve manual parameter tuning, grid search, random search, Bayesian search, and genetic algorithm tuning. Manual parameter tuning requires users to manually set and adjust the construction parameters required by the model, thus setting a ...

AI Technical Summary

Problems solved by technology

Manual parameter tuning requires users to manually set and adjust the construction parameters required by the model, thus setting a high threshold for users. Based on existing parameter optimization methods, such as grid search, the construction of XGBoost classification model When parameters are optimized, the optimization method is often mainly based on the search space given by experience or exhaustive, which not only fails to improve the ease of use of the classification model but increases the time complexity of the classification model

Images