In this paper, a novel method is presented to detect spam content on the web. It is based on classification and employs an idea from biology, namely, danger theory, to guide the use of different classifiers. The evaluation of content features of WEBSPAM-UK2007 data set using 10-fold cross-validation demonstrates that this method provides high evaluation criteria in detecting web spam. | International Journal of Computer Networks and Communications Security VOL. 3, NO. 4, APRIL 2015, 191–199 Available online at: E-ISSN 2308-9830 (Online) / ISSN 2410-0595 (Print) Web Spam Detection Inspired by the Immune System MAHDIEH DANANDEH OSKOUEI1 and SEYED NASER RAZAVI2 1 Department of Computer, Shabestar Branch, Islamic Azad University, Shabestar, Iran 2 Department of Electrical and Computer Engineering, University of Tabriz, Iran E-mail: 1r , 2 ABSTRACT Internet is a global information system, and search engines are currently the most common tools used to find information in web receiving query from the user, and present a list of the results related to user query. Web spam is an illegal way to increase web pages rank, and it tries to increase the rank of some web pages in the list of results by manipulating ranking algorithm of search engines. In this paper, a novel method is presented to detect spam content on the web. It is based on classification and employs an idea from biology, namely, danger theory, to guide the use of different classifiers. The evaluation of content features of WEBSPAM-UK2007 data set using 10-fold cross-validation demonstrates that this method provides high evaluation criteria in detecting web spam. Keywords: Artificial immune system, Web spam, Danger theory, Machine learning, Classification. 1 INTRODUCTION Artificial immune system is relatively a new science, and has been derived from the performance of body immune system when it encounters with pathogens. With regard to performance and complex defense mechanisms of natural immune system in living organisms against pathogens, researchers have designed artificial immune system by simulating this system, so that they can solve engineering problems. The research diversity created by using the method of artificial immune system indicates the ability to solve complex engineering problems thorough using .
