An Automated Detection System for Communication Messages Based on Statistical Content and Feature Analysis
The rapid development of information technology brings convenience to daily life while bringing more hidden dangers to network security.In order to improve the level of existing communication information security protection technology,a new type of automated detection model for communication information is proposed from a statistical point of view,based on the methods of multivariate statistical analysis,correlation analysis and principal component analysis to statistic and dimensionality reduction of network traffic data,and the introduction of the support vector machine for improvement.The experimental results show that the data statistics and dimensionality reduction under the multivariate statistical correlation and dimensionality reduction analysis method have the most obvious effect,while the detection model has the highest accuracy rate of 92.4%,the highest recall rate of 85.7%,the highest F1 value of 89.1%,and the lowest false alarm rate of 8.4%.From this,it can be seen that the proposed method of the study has certain feasibility and superiority,and the study aims to provide a new method for communication information security protection technology.
NETL
NSTL
万方数据
