A Lightweight Online Advertising Classification System using Lexical-based Features

Xichen Zhang, Arash Habibi Lashkari, Ali A. Ghorbani

Abstract

Due to the significant development of online advertising, malicious advertisements have become one of the major issues to distribute scamming information, click fraud and malware. Most of the current approaches are involved with using filtering lists for online advertisements blocking, which are not scalable and need manual maintenance. This paper presents a lightweight online advertising classification system using lexical-based features as an alternative solution. In order to imitate real-world cases, three different scenarios are generated depending on three different URL sources. Then a set of URL lexical-based features are selected from previous researches in the purpose of training and testing the proposed model. Results show that by using lexical-based features, advertising detection accuracy is about 97% in certain scenarios.

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Paper Citation


in Harvard Style

Zhang X., Habibi Lashkari A. and A. Ghorbani A. (2017). A Lightweight Online Advertising Classification System using Lexical-based Features . In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 6: SECRYPT, (ICETE 2017) ISBN 978-989-758-259-2, pages 486-494. DOI: 10.5220/0006459804860494


in Bibtex Style

@conference{secrypt17,
author={Xichen Zhang and Arash Habibi Lashkari and Ali A. Ghorbani},
title={A Lightweight Online Advertising Classification System using Lexical-based Features},
booktitle={Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 6: SECRYPT, (ICETE 2017)},
year={2017},
pages={486-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006459804860494},
isbn={978-989-758-259-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 6: SECRYPT, (ICETE 2017)
TI - A Lightweight Online Advertising Classification System using Lexical-based Features
SN - 978-989-758-259-2
AU - Zhang X.
AU - Habibi Lashkari A.
AU - A. Ghorbani A.
PY - 2017
SP - 486
EP - 494
DO - 10.5220/0006459804860494