A Fuzzy Ontology and SVM–Based Web Content Classification System

Visión electrónica, 2017

The incursion of the Internet has created new forms of information and communication, but it can also carry great dangers, when its use is related to inappropriate content, such as, access to harmful contents and the rise of new kinds of crimes. In this situation, automatic filtering systems identify improper Internet content. This paper describes the use of an algorithm, to automatically filter out inappropriate Web pages. To accomplish this (automatic filtering) task implementation method TAN (Tree Augmented Naive Bayes) is plasma. Data mining algorithms and computational learning for the extraction process, representation and classification of web pages are implemented.

International Journal of Computer Vision and Image Processing, 2(1), 75-90, January-March 2012, 2012

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Journal of Advances in Computer Research, 2015

Filtering of web pages with inappropriate contents is one of the major issues in the field of intelligent network's security. Having a good intelligent filtering method with high accuracy and speed is needed for any country in order to control users' access to the web. So, it has been considered by many researchers. Presenting web pages in an understandable way by machines is one of the most important preprocessing steps. Thus, offering a way to describe web pages with lower dimensions would be very effective, especially in determining the nature of web pages with respect to whether they should be filtered out or not. In this paper, we propose an automatic method to detect forbidden keywords from web pages. Next, we define a new representation of web pages in vector form which consists of weighted sum and frequency of forbidden keywords in different parts of web pages named RWSF. For this, a ranking dictionary of keywords including forbidden keywords is used. To evaluate the proposed method, 2643 pages consisting of 1311 normal pages and 1332 forbidden pages were used. Among these, 1851 pages were used to train the system and 792 pages were used for system evaluation. The system has been assessed using various classifiers such as: k-Nearest Neighbor, Support Vector Machines, Decision Tree and Artificial Neural Networks. Evaluation results indicate the high efficiency and accuracy of the proposed method in all classifiers.

2012 International Symposium on Innovations in Intelligent Systems and Applications, 2012

Internet is an infinite information repository that also contains harmful contents like; pornography, violence, and hate messages. It is very important to obstruct these kinds of contents from underage children not to adversely affect their development. Today, there are many commercial software products developed for this purpose. But the filtering capabilities of these commercial software are limited to text based and image based contents. Different techniques must be used to filter video based contents. This article describes an agent-based system which is developed for the detection of videos containing pornographic contents. Videos on the Internet can be divided into six groups as; anime, commercial, music, sitcom, sports, and porn related. The proposed system uses the Hidden Markov Model (HMM) based classification technique to classify the videos into these pre-defined categories with intelligent agents. Color features are extracted from each video frame and used as observation sequences in HMM for classification. According to the classification results, the videos, which are closely related to the category of sex and pornography, are filtered to the underage users. The test results obtained indicate that the classification has been satisfactory.

ACM SIGKDD Explorations Newsletter, 2006

The World Wide Web has now become a humongous archive of various contents. The inordinate amount of information found on the web presents a challenge to deliver right information to the right users. On one hand, the abundant information is freely accessible to all web denizens; on the other hand, much of such information may be irrelevant or even deleterious to some users. For example, some control and filtering mechanisms are desired to prevent inappropriate or offensive materials such as pornographic websites from reaching children. Ways of accessing websites are termed as Access Scenarios . An Access Scenario can include using search engines (e.g., image search that has very little textual content), URL redirection to some websites, or directly typing (porn) website URLs. In this paper we propose a framework to analyze a website from several different aspects or information sources, and generate a classification model aiming to accurately classify such content irrespective of acc...

2003

As Internet grows quickly, pornography, which is often printed into a small quantity of publication in the past, becomes one of the highly distributed information over Internet. However, pornography may be harmful to children, and may affect the efficiency of workers.

IJIIS: International Journal of Informatics and Information Systems

According to Pornography Statistics,more than 34 percent of Internet users exposeto pornography. There are 12 percent of the total number of websites and 72 million monthly visitors.Internet pornography (Internet Porn) is addictive to teenagers and kids around the world. The normal practice is to block those websites or filter out pornographyfrom kids.In order to do so, researchers has to find a way to detect and classify first. The pixel features including YCbCr range, area of human skin are chosen as pornographyfeatures because of their easy acquisition. C4.5 (Data mining technique)is applied to construct a decision tree. The purpose of this paper is to classify pornography images in a simple if-then rule. The accuracy of experimental result is 85.2%.

Visual Communications and Image Processing 2003, 2003

The paper addresses the problem of distinguishing between pornographic and non-pornographic photographs, for the design of semantic filters for the web. Both, decision forests of trees built according to CART (Classification And Regression Trees) methodology and Support Vectors Machines (SVM), have been used to perform the classification. The photographs are described by a set of low-level features, features that can be automatically computed simply on gray-level and color representation of the image. The database used in our experiments contained 1500 photographs, 750 of which labeled as pornographic on the basis of the independent judgement of several viewers.

The 11th IEEE International Conference on Networks, 2003. ICON2003., 2003

Web filtering is used t o prevent access to undesirable Web pages. In this paper we review a number of existing approaches and point out the shortcomings of each. We then propose a Web filtering system that uses text classification approach to classify Web pages into desirable and undesirable ones, and propose a text elassification algorithm t h a t is well suited to this application.

Lecture Notes in Computer Science, 2000

We present a method to detect automatically pornographic content on the Web. Our method combines techniques from language engineering and image analysis within a machine-learning framework. Experimental results show that it achieves nearly perfect performance on a set of hard cases.

IEEE Access, 2021

Nowadays, fraudulent and malicious websites are emerging as a harmful and very common problem on the Internet. It causes huge money losses and irreparable damage for both companies and particulars. To face this situation, governments have approved multiple law projects. This way, the legality on the Internet is being enforced and sanctions to those offenders who develop illegal or malicious activities are being imposed. However, governments still need a way to simplify the classification of websites into risky or non-risky, since most of this work is manual. This paper presents the DOmains Classifier based on RIsky Websites (DOCRIW) framework to detect domains that contain possible fraud or malicious content. It is based on two main components. The first component is a previously built knowledge base containing information from risky websites. The second one complements the system with a binary classifier able to label a website (as risky or not) considering just its domain. The system makes use of web information sources and includes host-based variables. It also applies similarity measures, supervised learning algorithms and optimization methods to enhance its performance. The presented work is experimental, rendering promising outcomes. INDEX TERMS Risky website detection, malware alerts, knowledge-based systems, similarity metrics, combination of information.

… Journal of Computer …, 2012

In the era of internet, users are keen to discover more in the web. As the number of web pages increases day-by-day malicious web pages are also increasing proportionally. This paper focus on detecting maliciousness in a web page using genetically evolved fuzzy rules. The above formed rules are filtered by Support Vector Machine and finally storing the result in a symbolic knowledge base, with appropriate weightage for each rule. This provides an insight to symbolic and non-symbolic intelligence in malicious web page detection.

Int. J. Comb. Optim. Probl. Informatics, 2016

In this paper, the problem to be solved is the visualization of undesirable contents on the Internet, both by children and young people. The a...

Proceedings of the 9th Joint Conference on Information Sciences (JCIS), 2006

Due to the flood of pornographic web sites on the internet, content-based web filtering has become an important technique to detect and filter inappropriate information on the web. This is because pornographic web sites contain many sexually oriented texts, images, and other information that can be helpful to filter them. In this paper, we build and examine a system to filter web pornography based on image content. Our system consists of three main processes: (i) normalized R/G ratio, (ii) histogram, and (iii) human composition matrix (HCM) based on skin detection. The first process is using the pixel ratios (red and green color channels) for image filtering. The second process, histogram analysis, is to estimate frequency intensities of an image. If an image falls within the range of training set results, it is likely to be a pornographic image. The last process is HCM based on human skin detection. The experimental results show an effective accuracy after testing. This would demonstrate that our hierarchical image filtering techniques can achieve substantial improvements.