Catching Webspam traffic with Artificial Immune System (AIS) classification algorithm
Muhammad Iqbal, Malik Muneeb Abid, Muqeet Ahmad · 2016
Everyday a large number of internet users are being encountered with web spamming where the search engines produce false ranking to web sites due to the use of unethical methods of Search Engine Optimization (SEO). The objective of the paper is to identify the spam traffic by using Artificial Immune System (AIS) classification algorithm. The paper presents chi square method for attribute selection of different machine learning methods, including proposed biological inspired Artificial Immune System for Spam Classification (AISSC) method, which provides a solution for supervised classification problem. In order to show the efficiency of proposed algorithm results are compared and analyzed with well-known classifiers i.e., Naive Bayes and J48. Experimental work is performed on Webspam-uk-2007 dataset. The results of proposed method show prominent achievements with increase in number of features to train the model.