Antyscam : Practical Web Spam Classifier

• Autorzy: Luckner Marcin , Gad Michał , Sobkowiak Paweł

Identyfikatory

DOI

10.24425/ijet.2019.130255

• Języki publikacji: EN

Abstrakty

EN

To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.

Słowa kluczowe

EN

web spam detection; spam detection; imbalanced sets classification; automatic classification; machine learning

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2019
• Tom: Vol. 65, No. 4
• Strony: 713--722
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Luckner Marcin

• Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00–662 Warszawa, Poland

autor

Gad Michał

• EO Networks, ul. Głuszycka 5, Warszawa, Poland

autor

Sobkowiak Paweł

• Sensi Soft, ul. Głuszycka 5, Warszawa, Poland

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Uwagi

The Antyscam was developed in EU POIG.01.04.00-14-031/11 project.