Pornography web pages classification with princpal component analysis and independent component analysis

The impressive growth of internet has made a new evolution of human life. Internet is an information superhighway but also the most unsecured place. Web users always need to take the risk for theft of information, spamming, virus threat and mental pollution of harmful resource. The illicit web conte...

Full description

Saved in:
Bibliographic Details
Main Authors: Lee, Zhi Sam, Maarof, Mohd. Aizaini, Selamat, Ali, Shamsuddin, Siti Mariyam
Format: Book Section
Language:English
Published: Penerbit UTM 2008
Subjects:
Online Access:http://eprints.utm.my/id/eprint/25487/1/MohdAizainiMaarof2008_PornographyWebPagesClassificationWith.pdf
http://eprints.utm.my/id/eprint/25487/
http://www.penerbit.utm.my/bookchapterdoc/FSKSM/bookchapter_fsksm01.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.25487
record_format eprints
spelling my.utm.254872012-05-16T07:11:30Z http://eprints.utm.my/id/eprint/25487/ Pornography web pages classification with princpal component analysis and independent component analysis Lee, Zhi Sam Maarof, Mohd. Aizaini Selamat, Ali Shamsuddin, Siti Mariyam QA75 Electronic computers. Computer science The impressive growth of internet has made a new evolution of human life. Internet is an information superhighway but also the most unsecured place. Web users always need to take the risk for theft of information, spamming, virus threat and mental pollution of harmful resource. The illicit web content such as pornography, violence, gambling, etc. have greatly polluted the mind of immature web users. Pornography perhaps is one of the biggest threats related to current children’s and teenagers’ healthy mental life. There are thousands of pornography sites on the internet can be easily found and detected. This will certainly become a detrimental factor to letting children and teenagers access internet without proper guidance. This fact makes web filtering systems are highly required in family and education environment. Web filter normally provide two major services which are protection against inappropriate content and preventing misuse of network [1]. Current filtering approaches such as URL blocking, keyword matching and rating system like PICS (Platform for Internet Content Selection) rating are widely implemented in today commercialize web filtering systems. The URL blocking technique will restrict or allow the web users to access web sites by checking required URL with sets of URL list stored in database. The problem of this technique is with current limited technology, it is hard to obtain the complete up–to-date URL list since there are an estimated 1 billion web pages being added daily [2]. This technique is costly in maintaining and insufficient against unknown web content. On the other hand, the trust issue is always an argument for PICS rating technique since the web publishers have the right to label whatever content to the metadata. Hence PICS is only suggested as a supplementary filtering technique due to its weakness against ever-changing web content. The keyword matching technique is designed to overcome the dynamic content issues; however it is not efficient during different subjects but having similar terminologies web pages. For instances this technique will block both pornography and gynecology web pages since intentionally we only need to block pornography web pages. Under-block and over-block are always the issues for this technique. Ordinary illicit web pages are constructed by mixing textual hyperlinked content with visual content. We could tackle the dynamic web content issues by using content based analysis approaches. Since most of the web pages contain textual information, so, we mainly focus on textual content based analysis. In fact, the approaches of current content based analysis mostly rely on machine learning process. Yu et al. [3] classify web pages by implementing their proposed framework, Positive Example Based Learning (PEBL) which uses support vector machine (SVM) as a classifier. Lee et al. [4] classify the documents with fuzzy learning technique and Selamat et al. [5] categorize the Japanese sport news with artificial neural network (ANN). Penerbit UTM 2008 Book Section PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/25487/1/MohdAizainiMaarof2008_PornographyWebPagesClassificationWith.pdf Lee, Zhi Sam and Maarof, Mohd. Aizaini and Selamat, Ali and Shamsuddin, Siti Mariyam (2008) Pornography web pages classification with princpal component analysis and independent component analysis. In: Advanced Computer Network & Security. Penerbit UTM , Johor, 31-50 . ISBN 978-983-52-0613-9 http://www.penerbit.utm.my/bookchapterdoc/FSKSM/bookchapter_fsksm01.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Lee, Zhi Sam
Maarof, Mohd. Aizaini
Selamat, Ali
Shamsuddin, Siti Mariyam
Pornography web pages classification with princpal component analysis and independent component analysis
description The impressive growth of internet has made a new evolution of human life. Internet is an information superhighway but also the most unsecured place. Web users always need to take the risk for theft of information, spamming, virus threat and mental pollution of harmful resource. The illicit web content such as pornography, violence, gambling, etc. have greatly polluted the mind of immature web users. Pornography perhaps is one of the biggest threats related to current children’s and teenagers’ healthy mental life. There are thousands of pornography sites on the internet can be easily found and detected. This will certainly become a detrimental factor to letting children and teenagers access internet without proper guidance. This fact makes web filtering systems are highly required in family and education environment. Web filter normally provide two major services which are protection against inappropriate content and preventing misuse of network [1]. Current filtering approaches such as URL blocking, keyword matching and rating system like PICS (Platform for Internet Content Selection) rating are widely implemented in today commercialize web filtering systems. The URL blocking technique will restrict or allow the web users to access web sites by checking required URL with sets of URL list stored in database. The problem of this technique is with current limited technology, it is hard to obtain the complete up–to-date URL list since there are an estimated 1 billion web pages being added daily [2]. This technique is costly in maintaining and insufficient against unknown web content. On the other hand, the trust issue is always an argument for PICS rating technique since the web publishers have the right to label whatever content to the metadata. Hence PICS is only suggested as a supplementary filtering technique due to its weakness against ever-changing web content. The keyword matching technique is designed to overcome the dynamic content issues; however it is not efficient during different subjects but having similar terminologies web pages. For instances this technique will block both pornography and gynecology web pages since intentionally we only need to block pornography web pages. Under-block and over-block are always the issues for this technique. Ordinary illicit web pages are constructed by mixing textual hyperlinked content with visual content. We could tackle the dynamic web content issues by using content based analysis approaches. Since most of the web pages contain textual information, so, we mainly focus on textual content based analysis. In fact, the approaches of current content based analysis mostly rely on machine learning process. Yu et al. [3] classify web pages by implementing their proposed framework, Positive Example Based Learning (PEBL) which uses support vector machine (SVM) as a classifier. Lee et al. [4] classify the documents with fuzzy learning technique and Selamat et al. [5] categorize the Japanese sport news with artificial neural network (ANN).
format Book Section
author Lee, Zhi Sam
Maarof, Mohd. Aizaini
Selamat, Ali
Shamsuddin, Siti Mariyam
author_facet Lee, Zhi Sam
Maarof, Mohd. Aizaini
Selamat, Ali
Shamsuddin, Siti Mariyam
author_sort Lee, Zhi Sam
title Pornography web pages classification with princpal component analysis and independent component analysis
title_short Pornography web pages classification with princpal component analysis and independent component analysis
title_full Pornography web pages classification with princpal component analysis and independent component analysis
title_fullStr Pornography web pages classification with princpal component analysis and independent component analysis
title_full_unstemmed Pornography web pages classification with princpal component analysis and independent component analysis
title_sort pornography web pages classification with princpal component analysis and independent component analysis
publisher Penerbit UTM
publishDate 2008
url http://eprints.utm.my/id/eprint/25487/1/MohdAizainiMaarof2008_PornographyWebPagesClassificationWith.pdf
http://eprints.utm.my/id/eprint/25487/
http://www.penerbit.utm.my/bookchapterdoc/FSKSM/bookchapter_fsksm01.pdf
_version_ 1643647599706636288
score 13.160551