Dynamic Web content filtering based on user's knowledge

Churcharoenkrung, N.; Kim, Y.S.; Kang, Byeong Ho

doi:10.1109/itcc.2005.137

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…This filtering is based on ML paradigm. ML techniques [3,9,10] and other strategies [8,7] have been used to exploit the Content-based filtering technique However the problem occurred is that content-based filtering on the contents of social network users has achieved few attention in the scientific community. For example Boykin and Roychowdhury [4] have proposed an automated system, exploiting the properties of social networks, able to find out the unsolicited commercial e-mail, spam and messages.…”

Section: Content Based Filteringmentioning

confidence: 99%

Classification of Messages and Content based Filtering From OSN User Walls

Talekar¹,

Nagori²

2015

IJCA

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Now a day Online Social Networks (OSNs) have become more popular among the internet users. But it has one major problem i.e. to prevent unwanted messages posted on user's personal space. OSNs provide very little support to this requirement. So we have proposed a system which makes the classification of messages and content based filtering from OSN user walls.This system permits OSN users to have a direct control on the messages posted on their walls by using a flexible rule-based system, that permit users to customize the filtering criteria to be put to their walls and a Machine Learning-based soft classifier that automatically classify and labels the messages in endure of content based filtering.

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Section: Content Based Filteringmentioning

confidence: 99%

Classification of Messages and Content based Filtering From OSN User Walls

Talekar¹,

Nagori²

2015

IJCA

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“…Neural networks model was used to create a knowledge base of the filtering system, which was trained by taking the samples of both pornographic and non-pornographic web pages. The development of a maintainable filtering system has been presented in [6], which is based on the approach of the expert systems. It easily maintains a knowledge base of that filtering system without the help of knowledge engineers.…”

Section: A Keyword Matching Based Filtering Techniquesmentioning

confidence: 99%

Text-Based Intelligent Content Filtering on Social Platforms

Khurshid

Khan

Bashir

2014

2014 12th International Conference on Frontiers of Information Technology

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Social platforms have become one of the popular mediums of information sharing and communication over the Internet today. People share all types of contents such as text, images, audio and video using these social platforms. Though information gained using these social platforms can be very useful for people around the globe, some of the user generated contents are very negative as they contain abusive, racial, offensive and insulting material. Thus, there is a need for an effective online content filtering technique which blocks these negative contents while not disturbing the access of users to rest of the contents available on these sites. Current techniques simply filter on the basis of URLs blocking and keyword matching or either rely on a large database of pre-classified web addresses. The problem is how to intelligently filter the negative contents, rather than filtering entire websites using their URLs or applying simple keyword matching techniques. In this paper we review a number of existing approaches to content filtering and propose an intelligent content filtering technique that uses sentiment analysis of the text and feature engineering methods to perform text classification.

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“…Any web content encrypted by IPsec ESP (Encapsulating Security Payload) is totally transparent to any intermediate network nodes, which makes the conventional web content filtering methods in IPv4 cannot work anymore. So the relevant technique of web content filtering [5] needs to be upgraded according to IPv6.…”

Section: Introductionmentioning

confidence: 99%

Framework of Web content filtering for IPv6

Lai

Yang

et al. 2010

2010 International Conference on High Performance Computing &Amp; Simulation

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IPv6 embraces various good features from the security perspective, but the improvements also bring us some new challenges for web content filtering. This paper presents a new framework of web content filtering in IPv6, which is helpful to purifying network environment. In this framework, using EM (Expectation maximization) algorithm, the active drift and self-learning mechanisms are proposed. Based on the new mechanisms, we can shield unknown illegal URLs effectively, and the illegal web content can be totally prevented from sneaking into the Intranet. Finally, our experimental results show that the performance of the system is very promising.

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Dynamic Web content filtering based on user's knowledge

Abstract: Abstract

Cited by 12 publications

References 11 publications

Classification of Messages and Content based Filtering From OSN User Walls

Classification of Messages and Content based Filtering From OSN User Walls

Text-Based Intelligent Content Filtering on Social Platforms

Framework of Web content filtering for IPv6

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