Web Content Filtering Mayur Lodha (mdl2130)

Agenda  Need of Filtering  Content Filtering  Basic Model  Filtering Techniques  Filtering  Circumvent Filtering  Problems with Filtering  Conclusion

NEED for Filtering

SAFE access to the INTERNET!

For Business

Keep kids SAFE on the Internet

PROTECT children for UNSUITABLE CONTENTS

Web Security and Anti-Virus Prevent malware and spam from entering the network

Content Filtering  Analysis of and Web content  Determine whether incoming data is harmful to the network or outgoing data includes intellectual property  Such data is then blocked from entering or leaving the network

Basic Model  Simple network layout  Network with traffic proxied

Simple Network Layout Connection is made by forwarding the uninspected traffic straight to the destination.

Network with Traffic Proxied Traffic is received by an application which alters the traffic in both the directions before sending to the destination.

Web Filtering Techniques  Firewalls  URL Based Filtering  Content Analysis

Firewalls  Basic level of web filtering  Inspects traffic to identify requested site to make decision (allow or block)  Black Lists: Undesirable Web addresses  White Lists: Desirable Web addresses

URL Based Filtering  Database of web addresses  Database loaded onto proxy servers, firewalls and other network infrastructure devices  Supports granular blocking

Content Analysis  Keyword Scanning  Image Scanning

Keywords Scanning  Tag words: Positive or Negative score  Block: Sum (score) > Threshold

Image Scanning

Filtering  Primary communication channel  Need to control SPAM and virus mails

Filtering Techniques  Channel/Response: Sender perform task  Heuristic Filters: Score words or phrases  Bayesian Filters: Mathematical Probability

Circumventing Web Filtering  Getting around them: Traffic is not passed through the filter.  Getting through them: Traffic is passed through the filter by obscuring the address of content and/or the content itself.

Proxy sites

Internet Filtering Test San Jose Public Library: Each filter blocks desirable protected content in its attempt to block objectionable content Type of Content TestedAccuracy Percentage Content of an Adult Sexual Nature – direct URL access 87% Content of an Adult Sexual Nature – keyword searches 81% Content not of an Adult Sexual Nature – direct URL access 86% Content not of an Adult Sexual Nature – keyword searches 69% Image Searches 44% Attachments 25% RSS Feeds 48% Catalog Searches 75% Database Searches 88%

Problem with filtering "It could be expected that allowed content would be blocked. If all pornographic content is to be blocked, other content with a resemblance in features will also be blocked; e.g. sex education, medical information, erotic content etc." - Dr Bjorn Landfeldt

Conclusion “All filters over block. All filters under block. No filter is 100% accurate because no one agrees on what being 100% accurate is.” - Lori Bowen Ayre

Thank You!!!