Web Content Filter: technology for social safe browsing Ilya Tikhomirov Institute for Systems Analysis of the Russian Academy of Sciences WWW:

2 State of art (1) The HTTP traffic is about 50 % of the information transfer in the Web. A part of Web is inappropriate (extremist and porno sites, social networks and music archives) for some categories of users (children, students, employees). The number of inappropriate sites grows constantly. Blocking access to inappropriate Web sites is the main goal of the content filters.

3 State of art (2) Modern content filters use predefined ban lists of URLs or IP addresses of inappropriate Web sites. Ban lists are formed manually by content filter developers or by network administrators. HTTP proxy servers became more popular in Web surfing. It makes usage of predefined ban lists inefficient and useless.

4 Disadvantage of current solutions Content filters use pattern matching for HTTP response. Content is blocked if any restricted signature was found in the HTTP response. Text is seen like a stream of bytes. No full-text analysis. Low recall and precision of filtering. The exponential growth of the Web and dynamically changing content cause an incomplete coverage of inappropriate Web resources.

5 Solution The solution is automatic classification based on full-text content analysis of the Web pages “on the fly”: System assigns documents to categories and denies access to inappropriate Web pages. The automatic classification method analyses Web pages according to terms importance in natural language text. Morphological analysis is used for text preprocessing.

6 Architecture of the Web Content Filter

7 Dynamic content filtering algorithm

8 Deployment of the Web Content Filter Main steps: 1.Forming inappropriate categories. 2.Preparing learning examples. 3.Running the automatic classifier in learning mode. 4.System setup, testing and parameters customization. 5.Running the automatic classifier in filtering mode.

9 Advantages Higher precision and recall of filtering than traditional methods. Transparency for users: no advanced settings of Web browsers and other Web applications are required. Adaptability to users’ behavior: only requested pages are examined. Scalability: distributed modules withstand load in big computer networks. Strictness level customization.

10 Usage Content Filtering for social safe Web browsing at: Educational institutions. State institutions. Companies. Home networks.

Contacts Institute for Systems Analysis of the Russian Academy of Sciences ,Moscow, pr. 60-let Octiabrya, 9 phone/fax: +7 (499)