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�0�m/2�����vZӺe��0�4�l,�qWr0�rFlL�x�5��xX�m�m�x�0�m�3�3�<�3�3�3�3�3�q�qe9��3�3�3�r�3�3�3�3���������������������������������������������������������������������x�3�3�3�3�3�3�3�3�3#	/:	Review on Web Spam Detection Techniques

Rekha Jain
Department of Computer Engineering
Poornima Institute of Engineering & Technology
Jaipur, India
E-mail: rekha.jain@poornima.org


Abstract: Today we all are directly and indirectly dependent on search engines because the Internet is a global information system that contains a wide amount of information. To filter the required information we use the search engines.  But Unfortunately Internet is suffering from DRIP syndrome i.e. Data Rich Information Poor. Means Internet is having a vast amount of data but sometimes search engines are not able to provide meaningful data to user in form of information. This is due to web spam phenomenon, where search results are not always as good as desired. Web spam is basically an illegal and unethical method through which the rank of internet pages can be increased by deceiving the algorithms of search engines.

Keywords: Spam  Detection  Techniques, Web Spam
Introduction
Search Engines play a very crucial role in searching the content because it is the point from where the information acquisition starts on the web. But due to web spam phenomenon they are not able to provide fruitful contents to user. As a result high quality searches become more challenging. 
A web spam is irrelevant or unsolicited messages sent over the Internet, typically to a large number of users, for the purposes of advertising, phishing, spreading malware, etc. Over the last decade researches on adversarial information retrieval have gained a lot of interest both from academia and industry because of web spam. In this paper firstly types of web spam are discussed then focus is given on methods of hostility with web spam.
KINDS OF WEB SPAM
Web spam can be introduced by intruders in many ways. The intruders introduce spam terms in web pages. The spam terms are irrelevant and are used for the purposes of advertising, phishing, spreading malware etc [1].�Majorly they are classified as Content-based web spam, Link-based web spam, Page-hiding based web spam and click spam.  Some of the possible ways are mentioned below-
Content-based web spam
i) Body spam
 This is one of the most popular and the simplest methods of spamming. Here the spam terms are placed inside the body tag of document. 
ii) Title spam
 At the time of displaying the search result, some search engines provide higher weights to the title of documents. In order to receive higher weights, spammers fill in this tag with spam terms. Therefore, if higher weight is dedicated to the words of page from search engine, then the page will receive higher rank. 
iii) Meta tag spam
The Meta tag of a web page allows the page designer to provide a short explanation about the page. Some search engine algorithms consider these pages on the basis of explanations that are included in meta tags. In this technique spammers create the meta tag that are totally irrelevant to web page contents but matches to search terms.  
iv) URL spam
Some search engine algorithms break URL of a web page into various terms and create long URLs that contain spam terms. For example, one of URLs created by this method is as follows: Buy-canon-rebel-20d-lens-case.camerasx.com [2] 
v)  Anchor text spam
Like document title, some search engines dedicate higher weight to anchor text terms because anchor text presents a summary about the document to which it is pointed. Hence, spam terms are sometimes placed in anchor text of a link. 
vi) Placing spam terms into copied contents
Here, spammers copy the texts from the web, and place spam terms at random places. The page that has been created by this spamming method is displayed in many query words.  
vii) Repetition of one or more special words
Spammers can obtain high ranks for considered page by repeating some of the special key words again and again in the web page.
Link-based web spam

i) Link farm
Link farm is a collection of pages or sites that are fully connected to each other [3]. This link farm supports the pages to improve their Pagerank.


ii) Link exchange
Some Web site owners help each other in order to improve their ranks by exchanging the links between sites. Web site owners obviously show this intention on web pages, or they may be sent to other site owners to request link exchange.  
iii) Buying the link
Some owners of web sites buy their own web sites from other sites by providing this kind of service.  
iv) Expired domains 
In this way spammers buy expired domains, and unused content is placed over it. Some expired domains may not be already admired, and the links of other sites may remain in these domains, and the validity of those domains is misused. 
v) Doorway pages
Doorway pages�are web�pages�that are created for spamdexing. This is for spamming the index of a search engine by inserting results for particular phrases with the purpose of sending visitors to a different�page. Web pages involve these kinds of links. Usually links in this doorway page point to the page of web site. Some spammers may create many doorway pages to obtain higher ranks.

Page-hiding based web spam

Page hiding-based web spam presents a different content to search engines to obtain high rank. This includes content hiding, cloaking and redirection techniques [4]. 

i) Content hiding
In this spamming technique terms and links are hidden when the browser renders a page. The more common approaches are using color schemes that render the elements in question effectively invisible.
ii) Cloaking
In this technique some web sites present different content to search engine rather than to users. Usually, web server can detect and identify company�s robots of search engines by IP address, and sends a content different form a page presented to normal users.  
iii) Redirection
Some web page uses different web spamming techniques to be seen by the search engine. When a user refers to a page through search result link, redirection is performed during loading a page.
Click Spam

As some search engines use click count an implicit feedback to tune ranking functions. For this spammers are eager to generate fraudulent clicks with the intention to bias those functions towards their websites. For achieving the goal spammers submit queries to a search engine and then click on links pointing to their target pages [5] [6]. To hide anomalous behavior they deploy click scripts on multiple machines or even in large botnets [7] [8]. The another way to generate fraudulent clicks comes from online advertising [9]. In this case, in reverse, spammers click on ads of competitors in order to decrease their budgets, make them zero, and place the ads on the same spot.

METHODS OF HOSTILITY WITH WEB SPAM

Hostile the behavior of Web spam is one of the major challenges for Web search engines. Anti-spam techniques focus on detecting varieties of spam strategies, such as content spamming and link-based spamming. Even though these anti-spam approaches have had much success, yet they encounter problems when fighting against a continuous barrage of new types of spamming techniques. These Anti-spam approaches use machine learning methods. The machine learning method does not require specification of any rules explicitly. Instead, a set of pre-classified documents that are known as training samples is needed. A specific algorithm is then used to �learn� the classification rules using this data. The machine learning has been widely studied and there are lots of algorithms suitable for this task.
Following section represents various approaches to hostile web spam-
A) Content Spam
i) Standard length of a word 
The standard length of a word can be one of parameter for the content based web page spam detection method. If the standard length is greater than maximum likelihood of spam page, the page can be considered as spam. As the spammer simply mixes the two or more keywords like freevideos, where there are two words free and videos because spammer stuffs the mixed words in to the body of a page. The spammer tags the malicious words in to the body of a page.
ii) Meta Tag Keyword Padding
Meta tag provides the information about the site that is given by the web master. The information like about the clients and search engine etc., are added in this section of HTML.
There are three parts of Meta tag  Description,   Keywords and  Robots. Code example :< Meta name=�description� content=�keywords of a page�/>
The search engine searches the keywords of Meta tag. If the Meta data keywords are not matching to the site content then the site is called spam web page [10].
iii) Number of Words in a Body
Number of words in the web page can be treated as another method to detect spam. In this the amount of applicable content of a page is measured. The web page is designed to give the information to the users. The contents of web page are extracted to find the number of words in the page tag. If it is less than a particular threshold then we can consider this measure as spam web page.

iv) Number of Stop-words in a Title
Another method is to find the fraction of stop words in a title. If the title of page contains more stop words, then it is valid web page because web page describes the information so it contains more stop-words. Usually spammer adds keywords but not the stop-words, so if fraction of stop-words is less than threshold then the page can be declare as spam web page.

v) Count of distinct words in body of a page
This method has maximum possibility of finding spam web page. Sometimes spammers repeat the same content on the page again and again. In this case the distinct count will be less than a threshold value and it can lead to spam web page.

vi) Amount of anchor text
Another interesting approach involves looking at the ratio of text to anchor text on a page. This can be on a page or site level. Websites with a high percentage of anchor text (to standard text) are more likely to be spam sites.

vii) Compressibility
In order to implement this mechanism to fight with Keyword stuffing, search engines can also look at compression ratios. Keyword stuffing refers to the practice of loading a webpage with�keywords�or numbers in an attempt to manipulate a site's ranking in Google search results. [11]. � Search engines often compress a page to save indexation and processing. They calculate the compression ratio (un-compressed divided by compressed) and according to it classify the page as spam.

viii) Query spam
This technique allows the spammers to seek query against various target terms and click on their own results. By looking 
at the pattern of the queries, in combination with other signals, these tactics would become statistically apparent.

B) Link Spam
i) TrustRank
This concept revolves around having "good neighbors".  Researches show that good sites link to good ones and vice versa. You are known by the company you keep. Spammers use this technique to increase the pagerank.

ii) Link stuffing
Here spammer would create a ton of low-valued pages and point all the links (even on-site) to the target page. Spam sites tend to have a higher ratio of these types of un-natural appearances.

iii) Nepotistic links
Nepotistic is an Italian word means here everything from paid links to traded ones (reciprocal). This may be a hazy area for SEOs, search engines most certainly believe link manipulation in any reciprocal form to be overt manipulation [12].

iv) Link farms
Link farm is a set of web pages created with the sole aim of linking to a target page, in an attempt to improve that page's search engine ranking. This is also called as Topological spamming.  Spammers create Link Farms because search engines look at the percentage of links in the graph compared to known "good" sites. Typically those looking to manipulate the engines will have a higher percentage of links from these locales. [13]

v) Temporal anomalies
Another area where spam sites generally stand out from other pages in the corpus is in the historical data. There will be a mean average of link acquisition and decay with "normal" sites in the index. Temporal data can be used to help detect spam sites participating in un-natural link building habits.

CONCLUSION
Ranking a page is an essential task in Web search. Interesting problems, for a Web page p, include: which other pages are the major contributors to the ranking score of p are, and how the contribution is made. In order to make more important page the spammers try to introduce various spam terms in web pages. These spam terms are totally irrelevant and users are not able to fetch meaningful content from the web. There is a lot of future work can be done for web spam detection. 

Spammers are using different tricks. In this paper various kinds of web spam detection as well as their hostility issues are discussed.
References
https://en.oxforddictionaries.com/definition/spam.
Alexandros Ntoulas, Detecting Spam Web Pages through Content Analysis, International World Wide Web Conference 2006
Saxena Akshay, Nigam Rohit, Page Rank Link Farm Detection, International Journal of Engineering Inventions e-ISSN: 2278-7461, p-ISSN: 2319-6491 Volume 4, Issue 1 (July 2014) PP: 55-59
Wu, B. and B.D. Davison Cloaking and Redirection: A Preliminary Study  in AIRWeb 2005
F. Radlinski. Addressing malicious noise in click through data. In Proceedings of the 3rd international workshop on Adversarial information retrieval on the web, AIRWeb 07, Ban�!, Canada, 2007
Z. Dou, R. Song, X. Yuan, and J.-R. Wen. Are click through data adequate for learning web search rankings? In Proceedings of the 17th ACM conference on Information and knowledge management, CIKM�08, 2008.
N. Daswani and M. Stoppelman. The anatomy of clickbot. a. In Proceedings of the First Conference on First
Y. Peng, L. Zhang, J. M. Chang, and Y. Guan. An e�!ective method for combating malicious scripts clickbots Spirin Nikita, Han Jiawei, Survey on Web Spam Detection: Principles and Algorithms, SIGKDD Explorations, Volume 13, Issue 2 pp 50-64
N. Immorlica, K. Jain, M. Mahdian, and K. Talwar. Click Fraud Resistant Methods for Learning Click-Through Rates. Technical report, Microsoft Research, Redmond, 2006.
Hunagund Kiran, Kumar Santhosh, Spam Web Page Detection based on Content and Link Structure of the Site, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 8, August 2015]
 �Irrelevant keywords� available at HYPERLINK "https://support.google.com/webmasters/answer/66358?hl=en"https://support.google.com/webmasters/answer/66358?hl=en
Brian D. Davison, Recognizing Nepotistic Links on the Web, HYPERLINK "http://www.aaai.org/Conferences/National/2000/aaai-iaai2000.html"AAAI-2000�HYPERLINK "http://archive.org/kurt/aaai2000.html"Workshop on Artificial Intelligence for Web Search, Austin, TX, July 30, and published in�Artificial Intelligence for Web Search, Technical Report WS-00-01, pp. 23-28, AAAI Press.
Link Farming: No Good Harvest, available at �http://www.seochat.com/c/a/link-trading-help/link-farming-no-good-harvest/�
Biography
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