Towards Realistic and ReproducibleWeb Crawl Measurements
Jordan Jueckstock, Shaown Sarker, Peter Snyder, Aidan Beggs, Panagiotis Papadopoulos, Matteo Varvello, Benjamin Livshits, Alexandros Kapravelos
Abstract
Accurate web measurement is critical for understanding and improving security and privacy online. Implicit in these measurements is the assumption that automated crawls generalize to the experiences of typical web users, despite significant anecdotal evidence to the contrary. Anecdotal evidence suggests that the web behaves differently when approached from well-known measurement endpoints, or with well-known measurement and automation frameworks, for reasons ranging from DDOS detection, hiding malicious behavior, or bot detection. This work improves the state of web privacy and security by investigating how, and in what ways, privacy and security measurements change when using typical web measurement tools, compared to measurement configurations intentionally designed to match "real" web users. We build a web measurement framework encompassing network endpoints and browser configurations ranging from off-the-shelf defaults commonly used in research studies to configurations more representative of typical web users, and we note the effect of realism factors on security and privacy relevant measurements when applied to the Tranco top 25k web domains. We find that web privacy and security measurements are significantly affected by measurement vantage point and browser configuration, and conclude that unless researchers carefully consider if and how their web measurement tools match real world users, the research community is likely systematically missing important signals. For example, we find that browser configuration alone can cause shifts in 19% of known ad and tracking domains encountered, and similarly affects the loading frequency of up to 10% of distinct families of JavaScript code units executed. We also find that choice of measurement network points have similar, though less dramatic, effects on privacy and security measurements. To aid the measurement replicability, and to aid future web research, we share our dataset and precise measurement configurations.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 050374a6-ed4b-4039-9f06-1366b5eac53dCited by top-tier papers13
- Reproducibility and Replicability of Web Measurement StudiesNurullah Demir, Matteo Große-Kampmann, Tobias Urban, Christian Wressnegger et al.WWW 2022 · 47 citations
- Towards Automated Auditing for Account and Session Management Flaws in Single Sign-On DeploymentsMohammad Ghasemisharif, Chris Kanich, Jason PolakisS&P 2022 · 25 citations
- Targeted and Troublesome: Tracking and Advertising on Children's WebsitesZahra Moti, Asuman Senol, Hamid Bostani, Frederik J. Zuiderveen Borgesius et al.S&P 2024 · 15 citations
- SoK: State of the Krawlers - Evaluating the Effectiveness of Crawling Algorithms for Web Security MeasurementsAleksei Stafeev, Giancarlo PellegrinoUSENIX Security 2024 · 12 citations
- Do Opt-Outs Really Opt Me Out?Duc Bui, Brian Tang, Kang G. ShinCCS 2022 · 9 citations
Builds on7
- Tranco: A Research-Oriented Top Sites Ranking Hardened Against ManipulationVictor Le Pochat, Tom van Goethem, Samaneh Tajalizadehkhoob, Maciej Korczynski et al.NDSS 2019 · 826 citations
- Online Tracking: A 1-million-site Measurement and AnalysisSteven Englehardt, Arvind NarayananCCS 2016 · 798 citations
- PhishFarm: A Scalable Framework for Measuring the Effectiveness of Evasion Techniques against Browser Phishing BlacklistsAdam Oest, Yeganeh Safaei, Adam Doupé, Gail-Joon Ahn et al.S&P 2019 · 129 citations
- Cloak of Visibility: Detecting When Machines Browse a Different WebLuca Invernizzi, Kurt Thomas, Alexandros Kapravelos, Oxana Comanescu et al.S&P 2016 · 93 citations
- Do You See What I See? Differential Treatment of Anonymous UsersSheharbano Khattak, David Fifield, Sadia Afroz, Mobin Javed et al.NDSS 2016 · 77 citations
Related papers
- The Representativeness of Automated Web Crawls as a Surrogate for Human BrowsingDavid Zeber, Sarah Bird, Camila Oliveira, Walter Rudametkin et al.WWW 2020 · 37 citations
- Apophanies or Epiphanies? How Crawlers Impact Our Understanding of the WebSyed Suleman Ahmad, Muhammad Daniyal Dar, Muhammad Fareed Zaffar, Narseo Vallina-Rodriguez et al.WWW 2020 · 42 citations
- You Get What You Sample: Evaluating Sampling Strategies for Web Security MeasurementsXuenan Zhang, Yuqing Yang, Giancarlo PellegrinoCCS 2026
- Web Execution Bundles: Reproducible, Accurate, and Archivable Web MeasurementsFlorian Hantke, Peter Snyder, Hamed Haddadi, Ben StockUSENIX Security 2025
- The First Early Evidence of the Use of Browser Fingerprinting for Online TrackingZengrui Liu, Jimmy Dani, Yinzhi Cao, Shujiang Wu et al.WWW 2025 · 7 citations
