An Illegal Billboard Advertisement Detection Framework Based on Machine Learning-Reference-Cited by-同舟云学术

An Illegal Billboard Advertisement Detection Framework Based on Machine Learning

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Big Data Technologies - ICBDT2019
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Author:

Liu Huxiao¹,Wang Lianhai¹,Zhang Weinan¹,Wang Wei¹

Affiliation:

1. Qilu University of Technology (Shandong Academy of Science), Shandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Provincial Key Laboratory of Computer Networks, Jinan City, China

Funder

Cooperation Fund for the Youth Doctor of Qilu University of Technology (Shandong Academy of Sciences)

Shandong Provincial Key Research and Development Program

National Natural Science Foundation of China

Publisher

ACM Press

Reference15 articles.

1. Dou, Rupeng, et al. "A Web-based advertising content analysis platform." 2010 International Conference on Future Information Technology and Management Engineering. Vol. 2. IEEE, 2010.

2. Zhang, Jiahao, et al. "Towards fast and lightweight spam account detection in mobile social networks through fog computing." Peer-to-Peer Networking and Applications 11.4 (2018): 778--792.

3. Lashkari, Arash Habibi, et al. "CIC-AB: Online ad blocker for browsers." 2017 International Carnahan Conference on Security Technology (ICCST). IEEE, 2017.

4. Masri, Rima, and Monther Aldwairi. "Automated malicious advertisement detection using VirusTotal, URL Void, and TrendMicro." 2017 8th International Conference on Information and Communication Systems (ICICS). IEEE, 2017.

5. Zhou Li, Kehuan Zhang, Yinglian Xie, Fang Yu, and XiaoFeng Wang. 2012. Knowing your enemy: understanding and detecting malicious web advertising. In Proceedings of the 2012 ACM conference on Computer and communications security (CCS '12). ACM, New York, NY, USA, 674--686. DOI: https://doi.org/10.1145/2382196.2382267.

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