URL-based web tracking detection using deep learning
10.23919/CNSM50824.2020.9269065
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/334688
Tipus de documentText en actes de congrés
Data publicació2020
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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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 accuracy, but at the expense of increasing their complexity and making them more difficult to maintain and configure by the end user. In this paper, we present a new web tracking detection method, called Deep Tracking Detector (DTD), that analyzes the properties of URL strings to detect tracking resources, without using any other external features. Consequently, DTD can easily be implemented in a browser plugin and operate in real time. Our experimental results, with more than 5M HTTP requests from 100K websites, show that DTD achieves a detection accuracy higher than 97% by looking only at the URL of the resources.
Descripció
©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Best Poster Award al 16th International Conference on Network and Service Management. November 2-6, 2020, IEEE Virtual Conference
CitacióCastell, 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.
GuardóDocument premiat
ISBN978-3-903176-31-7
Versió de l'editorhttps://ieeexplore.ieee.org/abstract/document/9269065
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