Using an Automatic Weighted Keywords Dictionary for Intelligent Web Content Filtering

Transcription

1 Journal of Advances n Computer Research Quarterly pissn: x eissn: Sar Branch, Islamc Azad Unversty, Sar, I.R.Iran (Vol. 6, No. 1, February 2015), Pages: Usng an Automatc Weghted Keywords Dctonary for Intellgent Web Content Flterng Najbeh Farz Vejouyeh 1, Jamshd Bagherzadeh 2 1) Islamc Azad Unversty of Shabestar Branch, Shabestar, Iran 2) Assstant professor, Computer Scence and Eng. Deptt, Urma Unversty, Urma, Iran Najbeh.Farz@yahoo.com; J.Bagherzadeh@urma.ac.r Receved: 2013/07/12; Accepted: 2014/05/26 Abstract Flterng of web pages wth napproprate contents s one of the major ssues n the feld of ntellgent network's securty. Havng a good ntellgent flterng method wth hgh accuracy and speed s needed for any country n order to control users' access to the web. So, t has been consdered by many researchers. Presentng web pages n an understandable way by machnes s one of the most mportant preprocessng steps. Thus, offerng a way to descrbe web pages wth lower dmensons would be very effectve, especally n determnng the nature of web pages wth respect to whether they should be fltered out or not. In ths paper, we propose an automatc method to detect forbdden keywords from web pages. Next, we defne a new representaton of web pages n vector form whch conssts of weghted sum and frequency of forbdden keywords n dfferent parts of web pages named RWSF. For ths, a rankng dctonary of keywords ncludng forbdden keywords s used. To evaluate the proposed method, 2643 pages consstng of 1311 normal pages and 1332 forbdden pages were used. Among these, 1851 pages were used to tran the system and 792 pages were used for system evaluaton. The system has been assessed usng varous classfers such as: k-nearest Neghbor, Support Vector Machnes, Decson Tree and Artfcal Neural Networks. Evaluaton results ndcate the hgh effcency and accuracy of the proposed method n all classfers. Keywords: Content based flterng, Forbdden keywords extracton, Rankng keywords, Web page representaton 1. Introducton The number of web pages has expanded greatly because of the fast growth of the World Wde Web. The ndexed Web contans at least 8.33 bllon pages untl July 8, Web page flterng has varous purposes. For nstance, protecton aganst mproper content s one of the major web page flterng purposes. Web provdes advantageous space for users to gan all knds of nformaton. But ths space has been flled wth a number of harmful web pages, lke pornography, volence, racsm, and so on. In 2001, the Onlne Computer Lbrary Center s annual revew found 74,000 adult webstes accountng for 2% of stes on the net, and they brought n profts of more than $1 bllon together; many were small scale, wth half makng $20,000 a year. Consequently, web flterng can be used to block access to pages that are aganst the establshed polcy. 101

2 Usng an Automatc Weghted Keywords N. Farz Vejouyeh, J. Bagherzadeh Another purpose of flterng s to avod msusng of the network. A survey on Internatonal Data Corporaton (IDC) proved that people spend one thrd of ther on-lne tme, on tasks other than ther job- related tasks. It s obvous that Internet accelerates the communcaton process and makes research actvtes more effectve, however t also has some problems obvously. Employees mostly use the nternet for personal actvtes such as on-lne shoppng, chattng wth frends or downloadng materal durng work hours, whch decrease productvty and responsblty of the company they work for. Hence, n recent years, plenty of researchers have obtaned notceable nterest n studyng and offerng a soluton to manage and flter mproper nformaton on the web. There have been plenty of flterng methods n the system, whch can be approxmately dvded nto four major categores as follows [1, 2, 3, 4]: Blacklst and whte-lst: Blacklst contans banned web stes, whch cannot be accessed, and whte-lst contans the pages, whch are allowed. Regardng a new web page, t s avalable or forbdden dependng on the requested URL, matchng ether blacklst or whte-lst. There s an obstacle here. Keepng the URL lsts complete and up to date s a very tough task. PICS: PICS (Platform for Internet Content Selecton) can develop rankng for web stes. There are usually two measures to rank the web pages. the frst one s selfrankng and the second one s other rankng. The dfference between two orgnates from the case that f the rankng results are gven by web publshers or not. Flterng systems can operate by means of rankng nformaton of web stes. The PICS s not an oblgatory labelng system, so the rankng nformaton s not always relable. Keywords flterng: Ths method s an easy approach to block access to web stes whch functon accordng to the occurrence of forbdden words. In ths method, a lst of forbdden words or phrases s often requred. Hence, the web page s blocked when the number of forbdden words n the web page s more than predefned lmts. The problem wth the keywords analyss based flterng systems s that they rely on the keyword lsts for a great deal, whch need great effort. Besdes, fndng enough partcular keywords n some felds s hard. The meanng of the word depends on the context. For example, f t s supposed to flter contents by matchng keywords for nstance a word lke "sex",t may mstakenly block web stes about genders. For ths reason, ths method wll unavodably cause over-blockng. In addton, ths method can easly be defected due to msspelled words. Intellgent approach to web content flterng: A web flterng system can use ntellgent approach to analyze the content. For nstance, tranng models or data mnng technques are effcent ways to classfy web contents automatcally. Content analyss s a worldwde method for web page flterng task because t s well-known that llegal web stes nclude partcular text, mage and other nformaton that can assst us to flter them. Supervsed learnng methods are used broadly n web page flterng systems. The problem wth supervsed learnng methods s that a great set of hgh-qualty labeled samples are needed, and they 102

3 Journal of Advances n Computer Research (Vol. 6, No. 1, February 2015) are hard to obtan. Sem-supervsed tranng methods are effcent when the avalable labeled sample set s not large. In ths paper, we have proposed a brand new method for web page representaton. In the proposed method, we have used weghted forbdden keywords dctonary to represent web pages. We have compared t wth TFIDF method n accuracy, tranng tme and memory usage. We also evaluated the effect of weghted forbdden keywords dctonary n accuracy of the proposed method by usng dfferent classfers. The remander of the paper s organzed as follows. In the secton 2 we start out revewng the related works on web flterng. The archtecture of our flterng system s descrbed n the secton 3. Web page classfcaton n our system s explaned n the secton 4. In the secton 5 we descrbe the proposed method for document representaton and weghted keywords dctonary. Expermental results are gven and dscussed n the secton 6, pror to the concluson n the secton Related Work Machne learnng methods such as k-nearest neghbors (knn), Neural Network, Decson Trees, Support Vector Machnes (SVM), Neural Networks (NN) are broadly used n web page flterng problems [5, 6, 7, 8, 9,17]. Du et al. [1] proposed a web flterng system that uses text classfcaton approach to classfy web pages nto desrable and undesrable ones. Smlartes between the nput web page and all tranng web page samples are averaged and compared wth a threshold to determne the label of the nput page. The system was traned wth a tranng dataset of 487 adult URLs, wthout any non-adult URLs and we used a database that ncluded 329 adult URLs and 587 non-adult URLs to test the system performance. Ther method acheved a hgh accuracy on the data set contanng adult texts from the adult category of Yahoo. Because the styles of pornography texts and stores are not the same, so ths approach cannot work well n the real world [10]. In [10], Wu et al. ntroduced a system lke a Cellular Neural Network word net to extract and reflect semantc and statstc aspects of texts. They analyzed dfferent types of keywords alongsde obvous keywords, hdden keywords and logcal keywords. SVM was appled as a classfer. In order to evaluate the performance of ther system, they used a dataset contanng 3162 Chnese texts among them 577 were trcky texts, 585 texts were related to sex but normal at the same tme and 2000 normal texts. 300 trcky texts, 300 sex-related normal texts and 1000 normal texts were used as tranng data, and the rest acted as test data. Also they gathered lst of 109 expressve terms contanng 29 apparent keywords, 33 hdden keywords and 47 logcal keywords. Ther expermental results showed that three knds of keywords can mprove the recognton rates notceably. They obtaned the best classfcaton rate usng the CNN- 103

4 Usng an Automatc Weghted Keywords N. Farz Vejouyeh, J. Bagherzadeh lke word net to extract aspects of texts too. It affrms that CNN-lke word net can accurately represent the semantc features of trcky texts. Chen et al. [11] frst used a C4.5 decson tree to classfy nput pages nto three classes of contnuous texts, dscrete texts, and mage pages. A CNN net s appled to recognze the semantc relatons wthn contnuous texts and a naïve Bayesan algorthm s adopted to dentfy dscrete texts. After that, a fuson classfer based on Bayes Theorem approach ntegrated texts and mages and 91.8% classfcaton rate was ganed over 1500 sample pages. Usng only URLs and keywords nstead of a content based analyss, as well as a small set of test data, and relatvely low accuracy rate are some shortcomngs of ther work [12]. He et al. [2] used a sem-supervsed framework for web page flterng. The Adaboost algorthm was used as a classfer. The expermental results show that sem-supervsed learnng approach outperforms supervsed method when avalable labeled sample set s small. Feature reducton should be employed to decrease the number of feature terms to an acceptable level before flterng. In [13] authors proposed to use a rough set to reduce orgnal feature terms. After selectng features, all web pages were represented by the feature vector wth the weghtng functon. They also presented a brand new coeffcent weghted method based on rough set to Bayesan formula. The method mproves flterng performance but t s not very effcent to ncrease flterng correctness. In [19], Ma proposed a neural network method for determnng the exstng status of a requested URL n the large prohbted collecton. The smulaton results show superor performances n both memory requrement and speed, comparng wth a database mplementaton on the same PC. 3. Flterng Archtecture We use the combnaton of the three methods ncludng black lst, keyword blockng and ntellgent content flterng for web page flterng. The formulaton of our system archtecture s as follows: 1) URL s launched. 2) If the ste exsts n the blacklst, block the page and stop. 3) Load the page's HTML source code. 4) If the frequency of the forbdden keywords n the page s more than a predefned threshold, classfy the page as forbdden page and go to the (6). 5) Analyze the content of the web page and make a further decson on the ste regardng whether to allow access or deny t. 6) Block the page f t s judged as a forbdden page and update the blacklst. Fgure 1 shows the general archtecture of our flterng system. 104

5 Journal of Advances n Computer Research (Vol. 6, No. 1, February 2015) Web Page Classfcaton Fgure 1. Flterng Archtecture Machne learnng technques provde us powerful ways to automatcally predct forbdden web pages usng manually classfed web pages. Fgure 2 llustrates the general schema of the proposed approach. It conssts of two phases: generatng a predcaton model phase and detecton phase. For web page representaton, the web pages have to be transformed from the full text verson to web page vectors. The Frst step conssts of tokenzaton, stop word removal and word stemmng to make a vocabulary, where each term occurs at least once n a certan number of web pages. In the second step, we prepare the forbdden keywords usng vocabulary and calculate ther ranks. After that we represent all the tranng web pages as vectors of 18 features usng the rankng dctonary of forbdden keywords obtaned n the prevous step. Web page vectors are used as nputs to learn and make a model (classfer) for predcaton. In the detecton phase a new web page s converted to ts correspondng vector usng forbdden keywords and ther ranks, then the classfer classfes t. 105

6 Usng an Automatc Weghted Keywords N. Farz Vejouyeh, J. Bagherzadeh 5. Web Page Representaton Fgure 2. General schema of the proposed approach Algorthms that can mprove the classfcaton effcency whle mantanng accuracy, are hghly desred. Nevertheless, web page representaton s one of the preprocessng technques that s used to reduce the complexty of the documents and make them easer to handle. Web page representaton s an mportant aspect n web page classfcaton, whch denotes the mappng of a web page nto a compact form of ts content. 5.1 Feature s vector wth the TFIDF weghtng functon A web page s typcally represented as a vector of term weghts (word features) from a set of terms (vocabulary). Vocabulary s the set of all dstnct words and other tokens occurrng n any web page from tranng dataset [18]. A major characterstc of the web page classfcaton problem s the extremely hgh dmensonalty of web page data. After selectng feature subsets, all documents were represented by the feature vector wth the TFIDF weghtng functon. That s, the weght of term t n document dj s calculated by 106

7 Journal of Advances n Computer Research (Vol. 6, No. 1, February 2015) f j N W j tfdf ( t, d j ) log M n f k 1 2 kj (1) Where f j denotes the number of tmes, t occurs n document dj, n(t) the number of documents n whch t occurs at least once, N the total number of documents, M s the sze of the feature subset. 5.2 Feature s vector wth the WRSF representaton's method Hammam et al. [4, 14, 15] use another method to represent web pages. They represent web pages as vectors of numbers, whch show numbers and frequences of forbdden keywords n dfferent parts of web pages such as ttle, body, lnks, etc. As the speed of flterng s mportant, ths method s a good way for representng web pages. The created vectors would have less dmensons whch speed up creatng a classfer and consequently web page classfcaton. In all the papers, whch use forbdden keywords dctonary to represent web pages, dctonary s made by experts based on forbdden groups, except the method of [15], whch creates sem-automatc dctonary based on n-grams that has hgh accuracy n contrast to manual and automatc methods. In sem-automatc methods there s a need for experts to select keywords whch are cost consumng and error prone. In ths paper we propose an automatc method based on Ch-square [9] to select forbdden keywords based on tranng documents. The term-goodness measure s defned as: X ( t ) N ( a d b c ) ( a b )( a c )( d b )( c d ) (2) Where a s the number of tmes t occurs n the forbdden web pages, b s the number of tmes t occurs n the normal web pages, c s the number of forbdden web pages wthout t, d s the number of normal web pages wthout t and N s the total number of webpages. Usng ths formula we can choose a number k of keywords as forbdden keywords where ther goodness s more than the predefned threshold. In all provded papers and systems whch use forbdden keywords dctonary to represent web pages, number and frequency of forbdden keywords have been consdered as man features. These methods gve equal mportance to all forbdden keywords of dctonary. However, when we need k number of keywords, all of them are not equally ncorporated n forbdden webpages. We can have hgh accuracy by rankng forbdden keywords of dctonary and take nto account the weghted sum and frequency of forbdden keywords nstead of number and frequency of forbdden keywords. We have selected a number of words and have normalzed ther rankng wth respect to ther 107

8 Usng an Automatc Weghted Keywords N. Farz Vejouyeh, J. Bagherzadeh mnmum and maxmum values and mapped them nto the (1, 40) nterval. Then we represent web pages as vectors of 18 features usng the rankng dctonary of forbdden keywords. Textual and profle features that we used to represent web pages are shown n Table 1. Weghted sum and frequency of forbdden keywords n dfferent parts of web pages are calculated by the followng formula: W eghted Sum Rank ( t ) n( t ) W eghted Frequancy Rank ( t ) n( t ) Rank ( t ) n( t ) m (3) Where n(t) s the number of tmes t occurs n the target part of the web page and m s the number of non-forbdden words n target part of the web page. Features nw-page wfw-page nw-body wfw-body nw-ttle wfw-ttle n-url nw-url n-lnk nw-lnk wfw-lnk n-mage nw-mage Wfw-mage nw-src nw-alt nw-meta wfw-meta Table 1. Selected features for web page representaton Descrpton Weghted sum of forbdden words that occur n the page Weghted frequency of forbdden words that occur n the page Weghted sum of forbdden words that occur n the body Weghted frequency of forbdden words that occur n the body Weghted sum of forbdden words that occur n the ttle Weghted frequency of forbdden words that occur n the ttle Number of URLs n the page Weghted sum of forbdden words that occur n the URLs Number of lnks n the page Weghted sum of forbdden words that occur n the lnks Weghted frequency of forbdden words that occur n the lnks Number of mages n the page Weghted sum of forbdden words that occur n the mages Weghted frequency of forbdden words that occur n the mage Weghted sum of forbdden words that occur n the attrbute src of the mg tag Weghted sum of forbdden words that occur n the attrbute alt of the mg tag Weghted sum of forbdden words that occur n the meta Weghted frequency of forbdden words that occur n the meta For example, the followng text s content part of tag Meta of a forbdden page, words of text that are n forbdden words dctonary are specfed n underlned form and rank of each words s gven n the aganst table. 108

9 Journal of Advances n Computer Research (Vol. 6, No. 1, February 2015) Brand New! We have revewed Shemale Sex Dates and t was awesome. Horny Shemale Lovers Take the Free Tour and see for yourself! Weghted sum and frequency of ths text s calculated as follows. Weghted Sum Forbdden keywords Shemale Sex Horny Rank W eghted Frequancy Words extracted from the text regardless of the forbdden words are defned n talcs after removng stop words and equals to 7. Weghted sum and frequency for texts related to rest of the Web page were calculated as sample and a vector consstng of 18 attrbutes s formed for each web page. 6. Expermental Results 6.1 Dataset descrpton To evaluate the proposed method, 2643 random samples of ODP 1 lnks have been selected from allowed and llegal categores. Among them 1311 web pages belong to the allowed category and 1332 pages belong to the forbdden category. Among selected samples, 933 legal web pages and 918 forbdden web pages have been randomly chosen as tranng dataset. Moreover, 792 web pages have been selected n order to assess system accuracy and effcacy that nclude 393 legal webpages and 399 forbdden web pages. 6.2 Performance measure Usually blockng and over-blockng rate are used for performance measurement n the flterng systems. Blockng rate measures the percentage of forbdden pages that the flterng system manages to block and over-blockng rate shows the rate of msclassfed normal pages as forbdden pages. They are defned by the followng equatons: BlockngRate Over BlockngRate TP TP TN FP FP FN (4) Where TP s the number of test web pages correctly classfed under forbdden web pages, FP s the number of test web pages ncorrectly classfed under forbdden web pages, TN s the number of test web pages correctly classfed under normal web pages, 1. Open Drectory Projects 109

10 Usng an Automatc Weghted Keywords N. Farz Vejouyeh, J. Bagherzadeh and FN s the number of test web pages ncorrectly classfed under normal web pages. These defntons are shown n Table 2. Table 2. The global contngency table Expert Judgment Yes No Classfer Yes TP FP Judgments No FN TN Another commonly used measure n flterng systems s accuracy that s defned n the equaton (5). Accuracy TP TN (5) N Where N s the total number of web pages. 7. Comparson Analyss To evaluate the proposed method, after attachng tranng web pages to each other, words n the pages are extracted and after removng stop words, the remanng words were stemmed. Porter algorthm [16] s used for word stemmng. The number of rooted words n the vocabulary was equal to after rootng the keywords. Forbdden keywords were selected usng the method mentoned n the secton 5. In the next stage, the correspondng vectors of web pages were formed n three ways. In the frst way (TFIDF), a web page was represented as a vector of words where the words are selected by CHI word selecton method [5]. In the second way (RSF), a web page was represented as a vector of numbers and frequences of forbdden keywords n dfferent parts of web pages. In the thrd way, a web page was represented as we proposed (RWSF), whch s ntroduced n the secton 5. Dfferent classfers ncludng Support Vector Machne, k-nearest Neghbor, Artfcal Neural Network and Decson Tree are used to evaluate all types of representatons. In our experments, all the classfers were obtaned from the framework Weka (Wtten and Frank 1999). We evaluate the performance of TFIDF method by varyng the number of features from 100 to1000. The results of our experments are shown n the Fgure 3. As seen n the fgure, SMO (a verson of SVM mplemented n Weka) has a hgh accuracy of 120 words, Neural Networks has a hgh accuracy of 160 words, and k-nearest Neghbor has a hgh accuracy of 100 words usng TFIDF method. 110

11 Journal of Advances n Computer Research (Vol. 6, No. 1, February 2015) Fgure 3. Comparson of the classfers n the TFIDF approach Fgure 4. Comparson of the classfers n the proposed approach Fgure 4 presents the comparson of the classfers wth number of dfferent keywords n the dctonary. Accordng to the results of experments, the SMO classfer has a hgh accuracy of percent wth 1000 keywords. The knn classfer wth k = 10 at best mood has accuracy of percent. Neural Network classfer has a hgh accuracy equal to percent wth dctonary ncludng 700 keywords. The Decson Tree classfer has the hgh accuracy of percent wth a dctonary that ncludes 800 forbdden keywords. To compare our method wth TFIDF, we selected the best result of the two methods n each classfer (has shown n Table 3) and calculated the percentage of ncrease or 111

12 Usng an Automatc Weghted Keywords N. Farz Vejouyeh, J. Bagherzadeh decrease n the accuracy, tranng executon tme, and memory usage for savng tranng data after preprocessng step by the followng formula: Result Result new old Result old 100 (6) Table 3. Comparng dfferent classfers n each method of web page representaton n the best way of accuracy TFIDF RWSF Accurac y Tranng Tme (S) Memory Usage (KB) Accuracy Tranng Tme (S) Memory Usage (KB) SMO DT ANN k-nn The result of our experments are shown n Fgure 5. Although our approach does not have much effect on ncreasng the accuracy of the system comparng to the tfdf method, t s very effectve n decreasng the tranng tme and memory usage. The evaluaton results of comparng RSF wth RWSF methods are shown n Fgure 6. As shown, the use of rankng dctonary s mentoned by varous classfers to evaluate ts effect n achevng hgher accuracy. Fgure 5. Percentage ncrease n accuracy, tranng tme and memory usage for savng data n the proposed method compared to TFIDF usng dfferent classfers. 112

13 Journal of Advances n Computer Research (Vol. 6, No. 1, February 2015) Fgure 6. Comparng the accuracy of flterng between use of forbdden keywords and weghted forbdden keyword wth dfferent classfers 8. Concluson Inventon of Web has made t the man place to publsh any knd of nformaton. There are varous types of nformaton ncludng a large number of napproprate web pages, whch are useless for some groups of people. Some organzatons need to flter access of ther communty to erotc pages. Recently, some ntellgent technques based on classfcaton methods of texts were proposed to prevent users to access forbdden web pages. In ths paper, we have proposed a new ntellgent automatc way to forbdden keywords dctonary formaton. We presented webpages usng varous features obtaned based on forbdden keywords dctonary. Then we assessed our flterng system usng dfferent classfcaton technques such as Decson Tree, Support Vector Machne, k-nearest Neghbor and Artfcal Neural Network. The results of all classfcatons show that the proposed method has hgh effcency. In ths paper, we flter web pages only usng textual nformaton of web pages. The accuracy needs to be further mproved by analyzng the varous multmeda n the web pages, ncludng audos, mages and vdeos. 9. References [1] Du R, Safav-Nan R, Suslo W. Web Flterng Usng Text Classfcaton. In Networks 2003 ICON2003 The 11th IEEE Internatonal Conference on; 28 Sept.-1 Oct. 2003; pp

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