Friday 29th of March 2024
 

Combating against Web Spam through Content Features


Muhammad Iqbal and Malik Muneeb Abid

Web spamming refers to use of unethical search engine optimization practices to gain better position on Search Engine Result Page (SERP). Making judgment on web-page to declare it as spam or ham is complicated issue because different search engines have different standards. Link-based spamming, cloaking and content spamming is main focus of different anti spam techniques. Even though these anti-spam techniques have had much success, however, these techniques still face problems when combating against a new kind of spamming techniques. This paper presents a usage of different machine learning methods which provides a solution for supervised classification problem. We have used WEBSPAM-UK-2007 public data set and in our experiments. The final results are compared and analyzed with well known classifiers. The results show that Jrip and J48 perform well compared to other two methods.

Keywords: Content Spam, Spam detection, supervised algorithms.

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ABOUT THE AUTHORS

Muhammad Iqbal
Muhammad iqbal was born in 1972 in Pakistan. He received B.Sc(Hons) and M.Sc degree in Computer Technology from Sindh University, Pakistan and MS in computer Science from SZABIST, Karachi, Pakistan. Since 2012, he is a PhD student in School of Information Sciences & Technology (SIST), Southwest Jiaotong University, Sichuan, Chengdu, PR China. His research interests are Network Security, Data Mining, Supervised Machine Learning algorithms and high speed data networks.

Malik Muneeb Abid
Malik Muneeb Abid was born in 1987 in Pakistan. He received B.Sc degree in Civil Engineering from U.E.T Taxila, Pakistan and MS degree in Transportation Engineering from NUST, Pakistan. Since 2013, he is a PhD student at School of Transportation and Logistics, Southwest Jiaotong University, Sichuan, Chengdu, PR China. His research interests are Network Robustness, Transportation network modeling and simulation, Data Mining, Supervised Machine Learning algorithms. He is member of IAROR and PEC.


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