Web spam denotes the manipulation of web pages with the sole intent to raise their position in search engine rankings. Since a better position in the rankings directly and positively affects the number of visits to a site, attackers use different techniques to boost their pages to higher ranks. In the best case, web spam pages are a nuisance that provide undeserved advertisement revenues to the page owners. In the worst case, these pages pose a threat to Internet users by hosting malicious content and launching drive-by attacks against unsuspecting victims. When successful, these drive-by attacks then install malware on the victims machines. In this paper we introduce a clustering and classification approach to detect spam web pages in the list of results that are returned by a search engine. Initially, we apply K-nearest neighbor approach for clustering. And then we will apply K-means classification over those links for categorizing them as either spam or non-spam links.

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Data mining K-nearest neighbor K-means algorithm Spam and Non-spam links Search Engine