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Top1. Introduction
Web spam detection is a process to find the techniques which uses (content spam, link spam, cloaking) to misguide the search engines to rank search results higher than that of what they are. While web spam prevention is a process which stops spam before it creates issues, such as, some spam filters which filter virus or unwanted email to enter into your email box (Dada et al., 2019). Content spam is populating the webpages with a lot of highly searched or monetizable keywords. Link spam is using trick / unnatural methods, such as, link exchange, buying links from some websites to get higher link score in short time. Cloaking is a method to provide different version of webpages to search engine spiders than that of what is shown to the real users (Najork, 2009). Spamming is a technique used to send uninvited messages (spam) to large number of recipients / websites for commercial advertising, non-commercial proselytizing or for fraudulent purpose of phishing. Web spam detection has always been a challenging area of research in social computing due to dynamic nature of Social Web. Basically, spam web pages do not meet mostly with the Google publisher guidelines1, that is why they lose their keywords specific rankings and must bear a huge financial loss. Spammers are using different kinds of black hat tactics to get a high rank in the search engines such as keyword stuffing or cloaking (Taweesiriwate, Manaskasemsak, & Rungsawang, 2012). All this kind of stuff which violates the terms of services of search engines is included in web spamming.
Web spam detection and Phishing attacks is one of the main issues for which many machine learning techniques are explored (Abutair and Belghith, 2017; Araujo & Martinez-Romo, 2010; Hu et al., 2016; Jiaet et al., 2012; Lin, 2009; San-Martín and Jimenez, 2017; Taweesiriwate et al., 2012). Some techniques are also applied to web spam detection by exploiting web page content features Moghimi and Varjani, 2016; Tan et al., 2016). In web spam detection, the websites are detected which do not comply with Google policies usually as a major search engine. For example, attacker attacks on the well-ranked website from their spam domain to down their competitors ranking from a search engine (Poggiet al., 2007).
Web spam Prevention is also one of the main issues after web spam detection is performed. Many people worked on prevention and have introduced many techniques of web spam prevention. In web spam prevention, web spam is prevented, which is generated by the spammer / attacker.Spam can be prevented manually by using some commercially available tools (e.g. Moz, Abuseipdb.com, Google Lighthouse plugin, Ahrefs SEO tool, etc) as well. Also, many automated techniques can be used for web spam prevention (Poggi et al., 2007).