Link Spam Detection based on DBSpamClust with Fuzzy C-means Clustering

TL;DR

This paper introduces DBSpamClust, a novel link spam detection algorithm that employs fuzzy C-means clustering to effectively identify and filter out spam links, improving search engine ranking accuracy.

Contribution

The paper presents a new link spam detection method combining DBSpamClust with fuzzy C-means clustering, enhancing spam filtering effectiveness over existing techniques.

Findings

DBSpamClust effectively filters web spam.

The method improves accuracy of link-based ranking algorithms.

Experimental results demonstrate high spam detection performance.

Abstract

Search engine became omnipresent means for ingoing to the web. Spamming Search engine is the technique to deceiving the ranking in search engine and it inflates the ranking. Web spammers have taken advantage of the vulnerability of link based ranking algorithms by creating many artificial references or links in order to acquire higher-than-deserved ranking n search engines' results. Link based algorithms such as PageRank, HITS utilizes the structural details of the hyperlinks for ranking the content in the web. In this paper an algorithm DBSpamClust is proposed for link spam detection. As showing through experiments such a method can filter out web spam effectively