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URL-based web tracking detection using deep learning

Author
Castell, I.; Poissonnier, T.; Manneback, P.; Barlet, P.
Type of activity
Presentation of work at congresses
Name of edition
16th International Conference on Network and Service Management
Date of publication
2020
Presentation's date
2020-11
Book of congress proceedings
CNSM 2020, 16th International Conference on Network and Service Management: November 2-6, 2020: virtual conference
First page
1
Last page
5
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
10.23919/CNSM50824.2020.9269065
Project funding
Architecting a knowLedge-defined 5G-enabLed network Infrastructure towArd the upcomiNg digital soCiEty
Repository
http://hdl.handle.net/2117/334688 Open in new window
URL
https://ieeexplore.ieee.org/abstract/document/9269065 Open in new window
Abstract
The pervasiveness of online web tracking poses a constant threat to the privacy of Internet users. Millions of users currently employ content-blockers in their web browsers to block tracking resources in real time. Although content-blockers are based on blacklists, which are known to be difficult to maintain and easy to evade, the research community has not succeeded in replacing them with better alternatives yet. Most of the methods recently proposed in the literature obtain good detection accu...
Citation
Castell, I. [et al.]. URL-based web tracking detection using deep learning. A: International Conference on Network and Service Management. "CNSM 2020, 16th International Conference on Network and Service Management: November 2-6, 2020, virtual conference". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1-5. ISBN 978-3-903176-31-7. DOI 10.23919/CNSM50824.2020.9269065.
Keywords
Deep learning, URL classification, Web tracking
Group of research
CBA - Communications and Broadband Architectures Lab

Participants