• 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