Show simple item record

dc.contributor.authorBalla, A.en
dc.contributor.authorStassopoulou, Athenaen
dc.contributor.authorDikaiakos, Marios D.en
dc.creatorBalla, A.en
dc.creatorStassopoulou, Athenaen
dc.creatorDikaiakos, Marios D.en
dc.description.abstractIn this paper we present a methodology for detecting web crawlers in real time. We use decision trees to classify requests in real time, as originating from a crawler or human, while their session is ongoing. For this purpose we used machine learning techniques to identify the most important features that differentiate humans from crawlers. The method was tested in real time with the help of an emulator, using only a small number of requests. Our results demonstrate the effectiveness and applicability of our approach. © 2011 IEEE.en
dc.source2011 18th International Conference on Telecommunications, ICT 2011en
dc.source2011 18th International Conference on Telecommunications, ICT 2011en
dc.subjectReal timeen
dc.subjectInformation technologyen
dc.subjectUser interfacesen
dc.subjectMachine learning techniquesen
dc.subjectDecision treesen
dc.subjectWeb crawlersen
dc.titleReal-time web crawler detectionen
dc.description.endingpage432 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: IBM Cyprusen
dc.description.notesUniversity of Cyprusen
dc.description.notesCyprus Tourism Organisationen
dc.description.notesConference code: 85747en
dc.description.notesCited By :7</p>en
dc.contributor.orcidDikaiakos, Marios D. [0000-0002-4350-6058]

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record