Network Measurements for Web Tracking Analysis and Detection: A Tutorial

Castell-Uroz, Ismael; Sole-Pareta, J.; Barlet‐Ros, Pere

doi:10.1109/mim.2020.9289071

Cited by 3 publications

(2 citation statements)

References 12 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e larger the number, the larger the template (where 1 means the template is 5 × 5, 2 means the template is 15 × 15, and so on, 10 means the template is 95 × 95); it can be seen that the NDC and NCC measurements are less affected by the size of the template, while the NIADD and NIASD measurements are greatly affected by noise under the small template, which seriously affects the matching results. e experimental results show that the size of the noise and the size of the template affect the value of the similarity measure to varying degrees, and the use of a suitable similarity measure can resist these effects to a certain extent [20]. e method to eliminate the influence of noise is mainly to reduce or eliminate the noise in the image by detecting noise, filtering, and other means.…”

Section: E Source Of Noise and The Noise Model Of Domain Visionmentioning

confidence: 99%

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Wang

Duan

2022

Security and Communication Networks

View full text Add to dashboard Cite

Moving target tracking technology is an important topic in the field of time domain sensors, which is widely used in medical diagnosis, intelligent robots, human-computer interaction, education, and entertainment. This article is based on the time domain vision sensor to explore the methods of sports target tracking and speed measurement. The article introduces the regional gray-scale correlation matching method in the vision sensor algorithm, the noise model of the time domain vision sensor, and the method of eliminating noise. At the same time, the tracking effects of SIFT algorithm, Mean Shift algorithm, and MSO algorithm on moving targets are studied, analyzing the ANF algorithm, TSML algorithm, and IHTLS based on the time domain visual sensor to compare the TUD-Crossing test set, the ETHBAHNHOF test set, and the SMOT data set; then the image matching was purified and analyzed based on the time domain visual sensor. The research results show that, in the method of exploring moving target tracking based on time domain vision sensor, when the improved SIFT algorithm uses feature adaptive fusion to describe the target, it can automatically allocate the proportion of color features and texture features, which improves the accuracy and reliability of moving target tracking. Among them, the DNP algorithm can control the final accuracy rate above 80%, and its recall rate is basically greater than 80%. In the case that the accuracy of rough matching is less than 50%, the final matching accuracy after processing by multiple purification algorithms is above 98%.

show abstract

Section: E Source Of Noise and The Noise Model Of Domain Visionmentioning

confidence: 99%

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Wang

Duan

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Users may voluntarily provide personal information on the web, such as filling out web forms, or such information may be indirectly collected without their knowledge through IP header analysis, HTTP request, search engine query analysis, JavaScript, etc [22]. However, recent studies have shown that the web tracking is widely used for malicious purposes, such as price discrimination, government surveillance, background scanning, and identity theft [24,35]. With the increasing use of the internet and digital technology, cyberattacks and surveillance have become more prevalent, posing significant threats to internet privacy and security: Malicious websites collect and sell user data to third parties without the user's knowledge or consent, which can lead to identity theft, financial fraud, and other harmful consequences.…”

Section: Introductionmentioning

confidence: 99%

Tracking the Web Tracking: Methods, Countermeasures, and Future Directions

Sim,

Heo,

Cho

2023

Preprint

View full text Add to dashboard Cite

A hidden shadow world that tracks Internet users' behaviors lies behind everyday websites. Web tracking analyzes online behaviors based on collected data and provides content that may interest the user. Web tracking collects vast amounts of data for various purposes, from sensitive personal information to trivial information such as settings and preferred information types, including the user's IP address, device, and search history. Although web tracking is largely a legitimate technology, there is a steady increase in illegal user tracking, data leakage, and illegal sale of data. Therefore, the need for techniques capable of detecting and preventing web trackers is becoming more important. This paper introduces a general description of web tracking technology, related research, and a website measurement tool that can identify web-based tracking. It also introduces techniques for preventing web tracking and discusses future research directions.

show abstract

TrackSign-labeled web tracking dataset

Castell-Uroz

Barlet‐Ros

2023

Computer Networks

View full text Add to dashboard Cite

Network Measurements for Web Tracking Analysis and Detection: A Tutorial

Cited by 3 publications

References 12 publications

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Tracking the Web Tracking: Methods, Countermeasures, and Future Directions

TrackSign-labeled web tracking dataset

Contact Info

Product

Resources

About