Web Spam Detection Using Multiple Kernels in Twin Support Vector Machine

Transcription

1 Web Spam Detecton Usng Multple Kernels n Twn Support Vector Machne ABSTRACT Seyed Hamd Reza Mohammad, Mohammad Al Zare Chahook Yazd Unversty, Yazd, Iran mohammad_6468@stu.yazd.ac.r chahook@yazd.ac.r Search engnes are the most mportant tools for web data acquston. Web pages are crawled and ndexed by search Engnes. Users typcally locate useful web pages by queryng a search engne. One of the challenges n search engnes admnstraton s spam pages whch waste search engne resources. These pages by decepton of search engne rankng algorthms try to be showed n the frst page of results. There are many approaches to web spam pages detecton such as measurement of HTML code style smlarty, pages lngustc pattern analyss and machne learnng algorthm on page content features. One of the famous algorthms has been used n machne learnng approach s Support Vector Machne (SVM) classfer. Recently basc structure of SVM has been changed by new extensons to ncrease robustness and classfcaton accuracy. In ths paper we mproved accuracy of web spam detecton by usng two nonlnear kernels nto Twn SVM (TSVM) as an mproved extenson of SVM. The classfer ablty to data separaton has been ncreased by usng two separated kernels for each class of data. Effectveness of new proposed method has been expermented wth two publcly used spam datasets called UK-2007 and UK Results show the effectveness of proposed kernelzed verson of TSVM n web spam page detecton. Indexng terms/keywords Search Engne, Web Spam Page Detecton, Machne Learnng, Twn Support Vector Machne (TSVM), Multple Kernels. 1.Introducton The ever-ncreasng volume of nformaton on the Internet has made search engnes vtal tools for nformaton extracton. From a theoretcal pont of vew, search engnes are nformaton retreval tools responsble for downloadng, ndexng, processng, retrevng, and rankng of nformaton [1]. Malcous content on the Internet, poses a unque challenge for search engnes; one that other nformaton retreval systems do not normally face. Malcous content refers to decetful nformaton created for the purpose of manpulatng a search engne s results. The open nature of the Internet allows any ndvdual to create and dstrbute arbtrary content. Thus, malcous content cannot be avoded. Web pages whch attempt to manpulate a search engne s results are the most mportant type of malcous content. Such pages are called Spam Pages. Other types of malcous content nclude spam queres, spam posts and Fshng pages. Most webste owners want to have hgher-rankng pages. Ths s because only 15% of users vst the second page of search engne results before changng ther query. Thus, search engnes are constantly tryng to assgn better ranks to the most relevant results. Ths mprovement ncreases ther revenue and popularty. Such attempts can be exploted, and led to the creaton of spam pages by profteers [2]. It should be noted that not all spam pages have commercal or explotve purposes. The contents of these pages dffer from the user s expectatons. They am to penetrate rankng algorthms and, therefore, can be classfed as spam pages. The man purpose of spam pages s to fraud rankng algorthms, wth the am of achevng a hgher rank among the top ten search engne s results of varous queres, as fast as possble [3]. Fg.1 llustrates a sample spam page. Snce the text n each page nclude multple valuable keywords, the content of the page s nsgnfcant for the user. From the perspectve of search engne, the most mportant effect of spam pages s dmnuton of search engne user s trust. Furthermore, the large number of spam pages, forces search engnes to allocate more resources (bandwdth, storage space, software) to crawlng, ndexng, storng, and processng contents of these pages. 1 P a g e

2 Fgure 1: An example spam page; although t contans popular keywords, the overall content s useless to a human user. Spam pages manly am to corrupt rankng algorthms n search engnes. Rankng s done by search engne whch estmates the qualty of a page wth respect to a partcular query. In other words, one scores s assgned to each retreved document. Documents are sorted n decreasng (or ncreasng) order of ther scores. Search engnes often use the followng measures to rank web pages: a) Relevance, measured as the degree of smlarty between the nput query and the text of the web page. b) Importance, nput query s gnored n t. Ths measure can be calculated as the number of tmes other pages are lnked to a certan page. As mentoned earler, spam pages are created wth the am of obtanng a hgh rank n a search engne s results. As a result, they are created wth the rankng algorthms n mnd [1]. The content of a spam page s organzed to match more queres. Therefore, a varety of useful keywords are used so that more user queres can be matched. Also, web spam content are changed based on the structure of web pages. Therefore, content rankng algorthms assgn hgher scores to these pages. If the goal s to match the target page to a certan request, then the target keywords need to be repeated multple tmes. In 2006, Najork et al. [4] presented an approach based on content analyss, usng examnaton of varous features of spam and non-spam pages. Ther fndngs formed a bass for dentfyng spam pages. Creatng a hgh number of lnks s another method used by spammers to mprove ther rank. In [6-8] a dfferent approach, based on the lnks between pages n the web graph, s used for detecton. The graph and patterns that emerge from t are used to dfferentate spam and non-spam pages. There have also been attempts to combne deas form both methods to acheve satsfactory results. In [5], spam pages are detected usng a combnaton of content-based and lnk-based methods. In recent years, user behavor has also been a decsve factor n detectng spam pages. Lu et al. [9] presented a behavor-based method. Modern technques for creatng spam pages nclude nformaton hdng, where users are encountered wth dfferent versons of pages compared to search engne crawlers. Varous methods of spam detecton have been proposed, ncludng those based on codng-style smlarty [18] and language pattern analyss [11]. Machne learnng algorthms have also been appled to page features [12-16]. The majorty of methods nvolved n creatng spam pages rely on content-based technques. Thus, analyzng the content features of these pages wll mprove much more effcency compared to other methods. Web pages have certan features that can be extracted usng content analyss. The values for these features are sgnfcantly dfferent across spam and non-spam pages. Snce the detecton problem seeks to label pages as ether spam or normal, t can be converted to a classfcaton problem wth two classes. Therefore, once 2 P a g e

3 features of page content are extracted and a sutable set of pages are labeled (normal or spam), a proper classfer can be used to determne the beng spam of unlabeled pages collected by a search engne. Machne learnng methods use classfcaton algorthms to extract and detect patterns among normal and spam pages. Usng more powerful algorthms can ncrease the accuracy of detecton. In prevous researches, Hdden Markov Model [12], Decson tree [13], Bayesan networks [14], and artfcal neural network [15] are used to separate spam nstances from non-spam ones. Many machne learnng algorthms have been used for the bnary classfcaton problem. Support Vector Machne (SVM) s one of these algorthms, whch offers favorable results. SVM despte s successfully appled to many problems of dfferentatng postve and negatve nstances, sutable accuracy has not been reported for web spam detecton by t. However, studes on other applcatons demonstrate that new extensons of SVM have sgnfcantly superor performance [19]. Such versons am to mprove ts performance on dfferent data through changes n SVM structure. SVM s bnary classfer, whch constructs a hyper-plane among nstances such that the dstance from the nstances s maxmzed and, thus, a good separaton s acheved. In an extended verson of t, namely the Twn SVM (TWSVM), two separate hyper-planes are used for each class nstances. Based on the nature of web pages and number of extracted features n them, ths verson of SVM can be used for web spam classfcaton. In some cases, samples cannot be separated lnearly by ther attrbutes. The concept of kernel has been ntroduced n machne learnng methods to solve ths problem. In ths approach, the data are frst logcally mapped to a hgher-dmensonal space. The new dmensons are created such that they can be lnearly separated wth more accuracy, usng hyper-planes [21]. In ths paper, TSVM wth two non-lnear kernels s used for spam page detecton. The man nnovaton of the proposed method s embeddng two dfferent sutable kernels for each hyperplane n TSVM. Expermental results on UK-2006 and UK-2007 datasets demonstrated the effectveness of the proposed method for detectng spam pages. The remander of the paper s organzed as follows. Secton 2 presents a revew of varous extensons to the SVM. The mportance of usng non-lnear kernels s consdered n secton 3. The detals of the proposed method are dscussed n secton 4. Secton 5 presents the expermental results. Fnally, n secton 6, the concludng remarks and suggestons for future works are offered. 2- SVM Extensons Support Vector Machne (SVM) s a non-statstcal bnary classfer, whch has attracted a lot of attentons n recent years. In ths method, all the nstances are used n conjuncton wth an optmzaton algorthm to fnd nstances that form the boundares of classes. Usng these nstances, an optmal lnear decson boundary s created to separate classes, and t s solved usng the Quadratc Programmng Problem (QPP) [23]. Ths classfer does not rely on statstcs; the tranng ponts are drectly used to determne the decson boundary between two classes. Proxmal Support Vector Machne (PSVM) s a new verson of the SVM, whch ams to reduce computatonal costs. In ths algorthm, two parallel hyper-planes, rather than one, are used to separate nstances. Generalzed Egenvalue Proxmal SVM (GEPSVM), uses two non-parallel hyper-planes, each of whch s placed near one of the two classes. Snce a large QPP must be solved, the algorthm becomes very costly. 3 P a g e

4 The mproved GEPSVM, called Twn SVM (TWSVM), elmnates ths problem by solvng two smaller nstances of QPP. Therefore, computatonal cost s decreased, whle the algorthm becomes more robust. Then, a varaton of TWSVM, namely the Least Square TWSVM was ntroduced. The fnal soluton of the algorthm s obtaned by solvng two lnear equatons, rather than two QPPs. Although the algorthm s easer, however t s less robust, due to ts senstvty to nose. Knowledge-based TWSVM s another verson of TWSVM, whch uses current nstances along wth prevous knowledge to separate nstances nto two classes. Compared to other knowledge-based algorthms, t offers less complexty tme. In order to mprove the structure of TWSVM, Smooth TWSVM was ntroduced. The most mportant change n ths verson of the algorthm s the elmnaton of QPP lmtatons. 3- Non-lnear Kernels Usng lnear models for classfcaton s much easer and faster than non-lnear counterparts. Lnear models work on data that can be separable lnearly. Often, developng of lnear models s more approprate than non-lnear ones, snce they are assocated wth lower complexty tme. However, n some cases, the nstances are not ntrnscally lnear. Such nstances must be mapped to a more dmensonal space, where a lnear model s applcable. Fg.2 llustrates how the data are mapped to a new space. Mappng to a hghdmensonal space s not always straghtforward manner and can mpose much more costs than prmary dmenson space. By offerng a way to reduce assocated costs, the dea of kernel addresses ths shortcomng. A kernel mplctly maps the data to a hgher-dmensonal space. In order to classfy non-lnear data n a lnear manner, the SVM needs to use the dea of a kernel, whch leads to enhanced classfcaton capabltes [21]. Fgure 2 dea of usng kernel Generally, n algorthms where data are n the form of dot products, the dea of a kernel can be appled. There are varous types of kernel functons. Polynomal, Gaussan, lnear, and fuzzy are more common kernel functons [21]. Usng kernel functons together wth SVMs s lead to performance mprovement of bnary classfcaton. 4- Proposed Method In ths paper, non-lnear kernels n the extended SVM are used to present a bnary classfer, wth superor performance n detectng spam pages. In the followng, the learnng structure of the SVM s dscussed n detal. Next, the non-lnear kernels n the TWSVM are ntroduced. Fnally, the proposed method,.e. Multple Kernel TSVM, s presented. As shown n secton of expermental results, ths method mproves the accuracy of detectng spam pages. 4 P a g e

5 4-1- TSVM The TSVM was frst ntroduced by Jayada [22], based on the normal SVM. In ths algorthm, nstead of usng a sngle plane and ncreasng ts margns toward bnary classfcaton, two non-parallel planes are used to separate nstances nto two classes. In ths classfer, each class s determned usng one of the hyper-planes. The procedure s demonstrated n Fg.3. Fgure 3: Operaton of Twn Support Vector Machne (TSVM) Assume that we are gven a set of data contanng postve and negatve nstances. In order to separate them, two equatons are formed for each hyper-plane, whch can segregate the data n TWSVM n a lnear fashon. F ( X ) W X b, F ( X ) W X b (1) T T where W W b, n, and b In the next step, two QPPs must be solved to obtan the optmal hyper-planes. QPPs can be challengng to solve; thus, they should be smplfed and solved usng Lagrange s method of undetermned coeffcents. Once the QPP s solved, the fnal equaton of the TWSVM to separate the nstances s obtaned n as follows. Class arg x w b mn (2) 1,2 w x w T where. s the absolute value and refers to classes of nstances Any new nstance can be classfed usng Eq(2) Usng kernels n the TWSVM As mentoned earler, n order to convert non-lnear classfcaton to a lnear one, nstances are mapped to a new hgh-dmensonal space before classfcaton phase. In secton 3, the dea of mappng the data to a new space was explaned n detal. Furthermore, n the prevous subsecton, the fnal equaton for the TWSVM classfer was presented. Here, we am to rewrte ths equaton usng a kernel functon. Eq(3) shows the fnal classfer by kernel desgn. 5 P a g e

6 Class arg K( x, C ) w b mn w K( x, C ) w 1,2 T (3) Where K s kernel functon whch maps data to hgher dmenson and any arbtrary kernel can be used to replace K, n ths equaton Multple non-lnear kernels n the TWSVM As prevously mentoned, n ths paper for the frst tme two separate kernels are adjusted n TWSVM. The ratonale behnd ths s the nature of spam pages as well as the dstrbuton of nstance. Ths paper tres to mprove classfcaton performance by desgnng two sutable kernels, both of whch are used to map the nput data to a new space. In the followng, detals of desgnng and matchng two approprate kernels for the TWSVM are dscussed. In subsecton 4-2, kernel desgn n the TSVM s explaned. Based on dmenson space of samples, an effcent kernel must be desgned to separate the samples both lnearly and optmally, n the new space. The choce or desgn of the kernel functon has a drect mpact on the effcency of the classfer algorthm. Kernel functons can be created as combnatons of standard kernels. The resultng kernel must satsfy Mercer s condton (Mercer s theorem checks the valdty of kernel functons). In ths paper, a sutable kernel functon for spam web pages s obtaned usng the method of fxed rule and a combnaton of two standard kernels. The frst kernel s lnear, whch s powerful and has low computatonal costs. The lnear kernel s shown n Eq(4). K( X, X j ) = X. X j + b (4) where X n, X j and b Lnear kernel s most effectve when the data are normally dstrbuted. Snce non-spam pages have a normal dstrbuton, ths kernel functon s used to map these nput nstances to a new space. The other kernel functon s the hyperbolc tangent, whch s used for non-lnear patterns n the data. The functon can be seen n Eq(5). K( X, X j ) = tanh (k X. X j - b) (5) where X n, X j and k, b Snce each kernel functon creates a smlarty matrx, t must be desgned such that the matrx for each nstance of the two classes matches the real patterns n the data. Therefore, the choce of the functon s very mportant. In ths paper, the lnear method s used, whch offers smplcty whle mantanng the advantages of both kernel functons. Eq(6) presents the lnear combnaton of the two functons. K = tanh(k X tran X t ' -b) 2 + ( X tran X t' ) (6) where X tran s the set of postve nstances (spam) and X t represents the entre set of data. After several experments, the coeffcents k and b were assgned 1 and 0, respectvely. A functon must satsfy Mercer s condtons before t can be consdered a kernel functon. The condtons are true for the proposed functon. 6 P a g e

7 After the approprate kernel s chosen, t must be adapted to the TSVM. As shown earler, n the TWSVM classfer the class of a new nstance s determned usng Eq(7). Class K( x, C ) w b arg mn 1,2 T (7) w K( x, C ) w In the proposed method, Eq(6) s appled to spam pages, whereas Eq(4) s used for normal nstances. As mentoned before, the TWSVM classfer uses two separate hyper-planes for each nstance, whch allows the mappng of the nput data to a new space. As a result, the hyper-planes become more effectve n segregatng of nstances. After the desgned kernel s adjusted to the TWSVM, the classfer must be traned. A porton of nstancesn each dataset s used for tranng purposes. The traned classfer can then determne whch class each new nstance belongs. 5- Expermental Results In ths secton, we present the detals of mplementaton and testng of the proposed algorthm, ncludng descrpton of datasets, evaluaton crteron, and expermental results Dataset In order to tran, execute, and determne the accuracy of the proposed algorthm, UK-2006 and UK-2007 datasets were used. These data sets have been used n numerous studes on spam pages. UK-2006 contans around hosts from.uk doman where 61.75% of documents are labeled as normal, 22.08% as spam, and 16.16% as unknown [26]. UK-2007 holds hosts from.uk doman where 94% of them are labeled as normal and the remanng 6% are spam. In ths paper we gnore documents wth Unknown label. The samples n two datasets are randomly dvded nto two parts, 75% as tran and other 25% as test sets Evaluaton In order to present the algorthm s results and compare them to those of other algorthms, a standard crteron s necessary for evaluaton. Accuracy s a measure of how many nstances are classfed correctly. It s popular n the context of decepton detecton. Compared to other measures, t s qute robust. TP TN Accuracy (8) P N In order to evaluate the accuracy of our classfer, we employed a technque known as ten-fold cross valdaton [4]. Ten-fold cross valdaton nvolves dvdng the judged data set randomly nto 10 equally-szed parttons, and performng 10 tranng/testng steps, where each step uses nne parttons to tran the classfer and the remanng partton to test ts effectveness Comparson of the proposed algorthm to SVMs Despte acceptable performance n classfyng varous types of data, the standard SVM algorthm has faled n the context of spam pages. The standard SVM was mplemented usng several kernels. A comparson of the results can be seen n Table 1. As evdent, the choce of kernel does not lead to sgnfcant changes n algorthm s performance, whch may be due to ts own structure. The standard SVM only uses one separatng plane; thus, t s not able to dfferentate between relatvely smlar spam and normal nstances. There are 7 P a g e

8 many smlar nstances wth opposng labels n the data sets. As a result, the SVM classfer demonstrates poor performance n web spam detecton. Table 1: Comparson between proposed method wth SVM and TWSVM by dfferent kernels Algorthm Kernel )%( Accuracy UK-2006 )%( Accuracy UK-2007 Lnear SVM RBF Hyperbolc Lnear TWSVM (One Kernel) RBF Custom Kernel TWSVM (Two Kernel) Kernel1 = Lnear Kernel2 = RBF Kernel1 = RBF Kernel2 = CK Kernel1 = Lnear Kernel2 = CK Comparson to other algorthms As mentoned before, dfferent classfers have been used for web sapm detecton, ncludng decson trees [14] and artfcal neural networks [16]. Furthermore, other studes such as [13] and [15] use dfferent algorthms, whch cannot be compared to the proposed algorthm due to ncompatble datasets. Tables 2 and 3 present our results along wth those of the most accurate algorthms n KU-2006, UK-2007 respectvely. Table 2: Comparson between proposed method wth other algorthm Algorthm Neural Network [16] HMM [13] Decson Tree[14] TWSVM MKTWSVM UK-2006 )%( Accuracy F-M Table 3: Comparson between proposed method wth other algorthm UK-2007 Algorthm Genetc Algorthm [24] Supervsed Neural Network [25] HTWSVM )%( Accuracy P a g e

9 MKTWSVM 95.6 MKTWSVM whch uses two hyper-planes to separate nstances of the two classes, wth two kernels for each, s able to acheve acceptable accuracy. Interestngly, n ths method, the nput data are mapped such that a lnear model can be used for dfferentaton. The choce and desgn of the kernel s an mportant ssue n ths problem. The proposed kernel n ths paper mproves the accuracy of a lnear classfer. Thus, a non-lnear kernel can be consdered the key component of a classfer. As shown n Table 3, nearly 3% of the ncrease n accuracy s assocated wth two separate kernels n the structure of the TWSVM. Advantages of ths method nclude low computatonal complexty snce the algorthm works n lnear space. 6- Concluson and Future Works In ths paper, by usng two non-lnear kernels n TSVM, hgh accuracy n detectng spam pages was acheved. Furthermore, n order to demonstrate the effectveness of non-lnear kernels, SVM and TWSVM were tested both wth and wthout kernel functons. The proposed method proved that two non-lnear kernels n TWSVM ncreases accuracy and reduces computatonal complexty. An avenue for future studes nvolves alterng the structure of extended versons of SVM so that accuracy s ncreased and computatonal complexty becomes more satsfactory. Moreover, snce the optmal combnaton of basc kernel functons for ether of the hyper-planes can mprove classfcaton accuracy, genetc programmng can be ncorporated to fnd the optmal combnaton of basc kernel functons. References [1] G. V. Cormack, M. D. Smucker, and C. L. A. Clarke, Effcent and effectve spam flterng and re-rankng for large web datasets, Informaton Retreval, pp. 1-25, [2] P. T. Metaxas and J. DeStefano, Web Spam, Propaganda and Trust, n Proceedngs of the 1st Internatonal Workshop on Adversaral Informaton Retreval on the Web, pp , [3] D. Fetterly, M. Manasse, and M. Najork, Spam, damn spam, and statstcs, n Proceedngs of the 7th Internatonal Workshop on the Web and Databases, pp , [4] A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly, Detectng spam web pages through content analyss, n Proceedngs of the 15th Internatonal Conference on World Wde Web,Chna Bejn Unversty, pp , [5] L. Becchett, C. Castllo, D. Donato, S. Leonard, and R. Baeza-Yates, Web spam detecton: Lnk-based and content-based technques, n The European Integrated Project Dynamcally Evolvng Large Scale Informaton Systems (DELIS): Proceedngs of the Fnal Workshop, Paderborn Unversty, pp , [6] D. Zhou, J. Huang, and B. Schölkopf, Learnng from labeled and unlabeled data on a drected graph, n Proceedngs of the 22nd Internatonal Conference on Machne learnng, Brazl Pugn Unversty, pp , [7] L. Becchett, C. Castllo, D. Donato, R. Baeza-Yates, and S. Leonard, Lnk analyss for web spam detecton, ACM Transactons on the Web (TWEB), Chna, vol. 2, pp. 1-42, [8] C. Castllo, D. Donato, A. Gons, V. Murdock, and F. Slvestr, Know your neghbors: web spam detecton usng the web topology, n Proceedngs of the 30th Annual Internatonal ACM SIGIR conference on Research and Development n Informaton Retreval, Amsterdam, The Netherlands, P a g e

10 [9] Y.Lu, R.Cen, M.Zhang, S.Ma, and L.Ru. Identfyng web spam wth user behavor analyss.proceedngs of the 4th Internatonal Workshop on Adversaral Informaton Retreval on the Web, pp. 9-61, [10] B. Wu and B. D. Davson, Cloakng and redrecton: A prelmnary study, Proceedngs of the 1st Internatonal Workshop on Adversaral Informaton Retreval on the Web (AIRWeb), pp. 7, [11] K. Chellaplla and A. Maykov, Cross-Lngual Web Spam Classfcaton,n Proceedngs of the 3rd Internatonal Workshop on Adversaral Informaton Retreval on the Web, pp , [12] Hmed, H.Najadat and Ismal Web Spam Detecton Usng Machne Learnng n Specfc Doman Features Journal. of Informaton Assurance and Securty, Vol. 38, pp , [13] A.Torab, K.Taghpour, and Sh.Khadv. "Web Spam Detecton: New Approach wth Hdden Markov Models." In Informaton Retreval Technology, Vol. 13, pp , [14] Tundalwar, R.Rashm, and M.Kulkarn. "New Classfcaton Method Based on Decson Tree for Web Spam Detecton.", Internatonal Journal of Current Engneerng and Technology, Vol. 8, Issue 9, pp , [15] Son, A.Anand, and A.Mathur. "Content based web spam detecton usng nave bayes wth dfferent feature representaton technque.", Journal of Engneerng Research and Applcatons, Vol. 3, Issue 5, pp , [16] Slva, M.Renato, T.A.Almeda, and A.Yamakam. "Artfcal neural networks for content-based web spam detecton." In Proc. of the 14th Internatonal Conference on Artfcal Intellgence (ICAI 12), pp [67] S. Büttcher, C. L. A. Clarke, and G. V. Cormack, "Informaton Retreval: Implementng and Evaluatng Search Engnes". Cambrdge, Mass.: MIT Press, [68] T. Urvoy, T. Lavergne, and P. Floche, Trackng web spam wth hdden style smlarty, n Proceedngs of the 2nd Internatonal Workshop on Adversaral Informaton Retreval on the Web (AIRWeb), p , [69] S.Bernhard, A.Smola, C.Wllamson, and L.Bartlett. "New support vector algorthms". Journal of Neural computaton, Vol. 4, Issue 7, pp , [20] Dng, J.Yu, B.Q, and H.Huang. "An overvew on twn support vector machnes." Journal of Artfcal Intellgence Revew, Vol. 3, Issue 7, pp 18-27, [21] S.Taylor, John, and N.Crstann. "Kernel methods for pattern analyss", Cambrdge unversty press, MA, [22] R. Khemchandan, Jayadeva. "Twn Support Vector Machnes for Pattern Classfcaton", IEEE Transacto on Pattern Analyss and Machne Intellgence, Vol. 29, No. 5, [23] S.Taylor, John, and N.Crstann. "Support Vector Machnes and Kernel Method", Journal of Artfcal Intellgence Revew, Vol. 12, No. 5, [24] A.Keyhanpour, and B.Moshr. "Desgnng a Web Spam Classfer Based on Feature Fuson n the Layered Mult- Populaton Genetc Programmng Framework", 16th Internatonal Conference on, pp IEEE, [25] A.Chandra, M.Suab, R.Beg. "Web Spam Classfcaton Usng Supervsed Artfcal Neural Network Algorthms", Advanced Computatonal Intellgence: An Internatonal Journal, Vol.2, No.1, January [26] Castllo, Carlos, D.Donato, L.Becchett, P.Bold, S.Leonard, M.Santn, and S.Vgna. " A reference collecton for web spam ", In ACM Sgr Forum, Vol. 40, No. 2, pp ACM, [27] M.Danadeh, S.N.Razav. "Web Spam Detecton Inspred by the Immune System", Internatonal Journal of Computer Networks and Communcatons Securty, Vol. 3, No. 4, pp , P a g e