Research And System Implementation Of Website Content Security Monitoring Based On SVW

With the rapid development of information technology,the information network,which is the core of information technology,has continued to grow at an unimaginable speed,affecting people's ways of learning,living and working.In the complicated network environment,the importance of website content security has become more and more prominent.In the case of netizens focusing on cyber-attacks,they have failed to pay attention to content-based security issues.Content-based security issues are semantic security filters that are directly attributable to the publi,Therefore,the corresponding analysis work for this problem is a very critical research project.Network information monitoring is an important part of the overall security system;Monitoring represents a reference to specific standardization and instrumentation to retrieve the information needed in a large flow of information.The essence of monitoring is the classification of content,and it is also an effective way to suppress the spread of bad information on the Internet.The current analysis of this problem focuses on the scope of monitoring information belongs to the dynamic information flow,the basic purpose of monitoring is to meet the objective needs of users,the basis of monitoring is the correlation between information and demand;The activities are also carried out in the exclusion activities.This article focuses on the shortcomings of detection schemes based on rule base matching.At the same time,the integration of word-removal and homonym matching is integrated,so as to effectively solve the defects in the old scheme that cannot effectively deal with the split words and homophones.Relying on the design ideas of machine learning,effectively avoiding defects such as reversal of word order and inability to learn by time.The training text set is represented by the matching dynamic and anti-document word frequency integration,which can effectively solve the non-related phrases existing in it,so as to effectively control the specific dimensions of the relevant vector,and at the same time strengthen the overall weight parameter of the keyword.This allows text with approximate semantics to be efficiently aggregated in specific aggregation points,The K-means algorithm is used to realize the effective classification operation for the text set,eliminating the associated text data,and achieving better accuracy performance based on the overall training speed.Comprehensive K-means and incremental Support Vector Machine(SVM)algorithm,which leads to a new model with more efficient efficiency and incremental learning.It also designs an incremental learning architecture based on the Storm architecture,compared to the common streaming architecture.In addition,on the basis of guaranteeing accuracy,it has achieved more ideal learning efficiency,it achieves more ideal learning efficiency,efficiently solves the requirements of high concurrency and large capacity in the security monitoring activities in the new era,and can effectively deal with various defects in the traditional scheme.It has a strong research significance for security monitoring.