SoK: After Decades of Web Tracker Detection, What's Next?
- URL: http://arxiv.org/abs/2605.02982v1
- Date: Mon, 04 May 2026 08:12:06 GMT
- Title: SoK: After Decades of Web Tracker Detection, What's Next?
- Abstract summary: We present the most comprehensive meta-science study on web tracker detection by systematizing and synthesizing the available knowledge.<n>We suggest a taxonomy, observe and evaluate trends, propose open research gaps, and recommendations.
- Score: 3.013252147267835
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Web tracking is an omnipresent phenomenon in today's web, affecting users in their day-to-day lives. Filter lists and blockers were invented to detect trackers and to protect users. Due to limitations of said tools, researchers developed web tracker detectors to replace them. No review constructed a universal perspective and classification of web tracker detectors until now. Past reviews focused either on the field as a whole or on web tracking techniques. In this SoK paper, we present the most comprehensive meta-science study on web tracker detection by systematizing and synthesizing the available knowledge. We conduct a systematic review, resulting in 59 primary and 16 supplementary studies out of a corpus of 832 papers. Based on these findings we suggest a taxonomy, observe and evaluate trends, propose open research gaps, and recommendations with which we aim to lay the foundations for future web tracker detection research. In addition, we conduct a limited reproducibility study to assess the validity of past studies and highlight emerging problems in this field.
Related papers
- Phishing Webpage Detection: Unveiling the Threat Landscape and Investigating Detection Techniques [0.03499870393443268]
Researchers have been actively working on advancing phishing webpage detection approaches.<n>The ever-evolving strategies employed by attackers, aimed at circumventing existing detection approaches and tools, present an ongoing challenge to the research community.<n>This survey presents a systematic categorization of diverse phishing webpage detection approaches, encompassing URL-based, webpage content-based, and visual techniques.
arXiv Detail & Related papers (2025-09-10T09:14:18Z) - SoK: Advances and Open Problems in Web Tracking [71.54586748169943]
Web tracking is a pervasive and opaque practice that enables personalized advertising, and conversion tracking.<n>Web tracking is undergoing a once-in-a-generation transformation driven by shifts in the advertising industry, the adoption of anti-tracking countermeasures by browsers, and the growing enforcement of emerging privacy regulations.<n>This Systematization of Knowledge (SoK) aims to consolidate and synthesize this wide-ranging research, offering a comprehensive overview of the technical mechanisms, countermeasures, and regulations that shape the modern and rapidly evolving web tracking landscape.
arXiv Detail & Related papers (2025-06-16T23:30:54Z) - Monocular Lane Detection Based on Deep Learning: A Survey [51.19079381823076]
Lane detection plays an important role in autonomous driving perception systems.<n>As deep learning algorithms gain popularity, monocular lane detection methods based on them have demonstrated superior performance.<n>This paper presents a comprehensive overview of existing methods, encompassing both the increasingly mature 2D lane detection approaches and the developing 3D lane detection works.
arXiv Detail & Related papers (2024-11-25T12:09:43Z) - Robust Recommender System: A Survey and Future Directions [58.87305602959857]
We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise.<n>We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training for defending against malicious attacks.<n>We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness.
arXiv Detail & Related papers (2023-09-05T08:58:46Z) - Machine Learning for Detection and Mitigation of Web Vulnerabilities and
Web Attacks [0.0]
Cross-site scripting (XSS) and cross-site request forgery (CSRF) have been a great concern in the field of web security.
Several ideas have been put forth that can be used to improve the performance of detecting these web vulnerabilities.
Machine learning techniques have lately been used by researchers to defend against XSS and CSRF.
arXiv Detail & Related papers (2023-04-27T18:27:26Z) - Deep learning for table detection and structure recognition: A survey [49.09628624903334]
The goal of this survey is to provide a profound comprehension of the major developments in the field of Table Detection.
We provide an analysis of both classic and new applications in the field.
The datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature.
arXiv Detail & Related papers (2022-11-15T19:42:27Z) - Rally and WebScience: A Platform and Toolkit for Browser-Based Research
on Technology and Society Problems [1.4511816961424293]
Deploying studies in participant browsers and devices is a promising direction.
Rally and sdkName are a platform and toolkit for browser-based research.
We report results from a one-month pilot study on news engagement, analyzing 4,466,200 webpage visits from 1,817 participants.
arXiv Detail & Related papers (2022-11-04T06:05:06Z) - Camouflaged Object Detection and Tracking: A Survey [7.056222499095849]
We review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view.
This article also addresses several issues of interest as well as future research direction on this area.
arXiv Detail & Related papers (2020-12-25T14:15:45Z) - Anomalous Example Detection in Deep Learning: A Survey [98.2295889723002]
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for Deep Learning applications.
We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches.
We highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.
arXiv Detail & Related papers (2020-03-16T02:47:23Z) - Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process [63.75363908696257]
We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
arXiv Detail & Related papers (2020-01-27T11:21:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.