DocumentCode
1770511
Title
Spam host classification using swarm intelligence
Author
Enache, Adriana-Cristina ; Patriciu, Victor Valeriu
Author_Institution
Fac. of Autom. Control & Comput. Sci., Politeh. Univ., Bucharest, Romania
fYear
2014
fDate
29-31 May 2014
Firstpage
1
Lastpage
4
Abstract
Web Spam, or Spamdexing, is a form of Search Engine Optimization(SEO) spamming that hinders the efficiency of search engines. These types of exploits use unethical methods in order to place a web page into the first rank. Sabotaging the quality of the results retrieved by search engines can lead users to mistrust the search engine provider. Moreover, spam websites can be a starting point for phishing or malware attacks. Over the last decade Web Spamming has become an important problem. This paper shows a spam host detection approach that uses swarm intelligence. We test our model on two datasets (WEBSPAM-UK2011 and WEBSPAM-UK2007) and show that it can obtain a good accuracy. Moreover, we compared our approach with other popular classifiers (C4.5, SVM and Logistic Regression ) and empirically demonstrated that it can outperform them in some cases.
Keywords
Internet; optimisation; search engines; swarm intelligence; unsolicited e-mail; WEBSPAM-UK2007; WEBSPAM-UK2011; Web spamming; search engine optimization spamming; spam host classification; spam host detection; spamdexing; swarm intelligence; Accuracy; Data mining; Feature extraction; Particle swarm optimization; Support vector machines; Training; Unsolicited electronic mail; ant colony classification algorithm; host spam; swarm intelligence;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications (COMM), 2014 10th International Conference on
Conference_Location
Bucharest
Type
conf
DOI
10.1109/ICComm.2014.6866669
Filename
6866669
Link To Document