2020 16th International Conference on Network and Service Management (CNSM) 2020
DOI: 10.23919/cnsm50824.2020.9269065
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URL-based Web Tracking Detection Using Deep Learning

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 th… Show more

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Cited by 7 publications
(3 citation statements)
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“…The main reason of this overhead relies in the number of resources Given that the problem is inherent to the use of pattern matching algorithms, a possible improvement would consist of using machine learning to reduce this overhead. Following this idea, we implemented a new browser plugin called Deep Tracking Blocker (DTB) that is based on a Deep Learning model proposed in [40]. This approach was shown to obtain a detection accuracy similar to traditional blacklists (97% precision), while moving the bulk of the pattern matching cost to an offline training phase.…”
Section: Discussion and Possible Improvementsmentioning
confidence: 99%
“…The main reason of this overhead relies in the number of resources Given that the problem is inherent to the use of pattern matching algorithms, a possible improvement would consist of using machine learning to reduce this overhead. Following this idea, we implemented a new browser plugin called Deep Tracking Blocker (DTB) that is based on a Deep Learning model proposed in [40]. This approach was shown to obtain a detection accuracy similar to traditional blacklists (97% precision), while moving the bulk of the pattern matching cost to an offline training phase.…”
Section: Discussion and Possible Improvementsmentioning
confidence: 99%
“…In this study, several state-of-the-art techniques were selected as baseline approaches and compared with the proposed method. These methods include URLNet [21], DURLD [46], DTD [47], FS-NB [48], TCURL [49], and PhishBERT [27].…”
Section: Baseline Approachesmentioning
confidence: 99%
“…Recently, several studies suggested anti-tracking methods to detect tracking behaviors and thirdparty cookies. In this context, Castell-Uroz et al in [18] suggested a new anti-tracking method that analyzes the characteristics of URL strings to discover tracking resources and without using any external features. This method is called Deep Tracking Detector (DTD).…”
Section: Gómez-boix Et Al Carried Out Similar Work Inmentioning
confidence: 99%