Information Security Filtering Technology Research Based On The Content Analysis

Because of the opening and the scale that increased day-by-day of the network, it has come to being a convenient means by which people exchange information freely. But at the same time, there are very great negative effects also, such as the spread of various kinds of superstition, pornography, violence, reactionary and some other illegal information, or leaking of secret information etc. But the traditional filtering technology, such as based on keywords, or based on IP address filtration cannot effectively to solve these problems in now.Because of this kind of demand, we carried on research to the analysis of the information filtering technology based on content of the network in order to filter the information security online. Methods based on statistics or knowledge is used. In this thesis, we have proposed a kind of filtering method based on content. According to users' filtering demand, set up the information characteristic filtering model. We consider two respects factors that statistics characteristic of the file and knowledge synthetically, draw support from the thought of the vector space model that consider the frequency and the length of the word to set up the vector space form of the text. Then the attributive character of the word is introduced to the vector space form to analyze the whole characteristic of the text. Because the filtering technology of statistics characteristic neglects the semantic restrain of the text, it can't really analyze the text intelligently and, win the better filtering result. We have introduced local semantic analysis and set up characteristic model from two respects of statistics characteristic andknowledge in this method in order to analyze and filter text efficiency.Preliminary test indicate that method put forward in the article can identify the false match and filter the text by content analysis efficiently. But it is a complicated and long course to analyze content of text in order to filter text intelligently; the method that we put forward is only a beginning step. There are a lot of questions need to be improved, such as the accuracy on word segmentation, characteristic representation of text and more introduction analysis of semanteme, etc. So it should be studied further in future work.