Research On Software User Behavior Credibility Analysis Model Based On Ensemble Learning

The continuous updating and iteration of software technology has promoted the continuous changes in social production and lifestyle.While promoting the construction and development of an information society,it has also brought various security problems to our software systems.For the security problems outside the software system,mainly through the network vulnerabilities and security flaws to attack the software and the data in the system,these attacks can be effectively protected by network security strategies and virus defense strategies.However,for the internal security problems of the software system,the legal users under certain circumstances make illegal and abnormal behaviors,which are often destructive,fatal.Since the attacker only abuses the privileges of the existing account without violating any restrictions,it is difficult to distinguish between the behavior of the attacker and the behavior of the legitimate account.Considering the fact that the legal user's abnormal behavior and benign user's behavior change regularly within the software system,it is unrealistic to monitor whether the user's behavior of the system is credible through the security prevention and control strategy,mainly because different users have their own use habits and behavior tracks when using different software systems,which are flexible and changeable.Therefore,in order to ensure the security of the software system and the credibility of user behaviors,the timely identification and management of abnormal user behavior risks need to evaluate the credibility of software user behaviors.The existing credibility analysis methods of Web software user behavior based on standard signatures or based on anomaly detection are relatively simple,fixed,and inflexible.This paper compares the effects of different individual learning algorithms and homogeneous ensemble learning algorithms on the basis of simulating the software user behavior Web log data set generated by the county and city government office system,and combines the obtained better effect algorithms to build the credibility analysis models of heterogeneous ensemble learning algorithms.And analyze the evaluation results and model optimization on different combinations of heterogeneous ensemble learning algorithm models to obtain the final software user behavior credibility analysis model.The experimental process mainly includes the generation,quantization and feature processing of dataset and the selection,training verification,parameter tuning of algorithm model.Experiments show that,on the simulated data set,through the heterogeneous ensemble learning algorithm model,the accuracy of software user behavior credibility analysis has been increased to more than 97%.