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2024 | OriginalPaper | Buchkapitel

Detecting Web Tracking at the Network Layer

verfasst von : Maximilian Wittig, Doğan Kesdoğan

Erschienen in: ICT Systems Security and Privacy Protection

Verlag: Springer Nature Switzerland

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Abstract

Third-party tracking allows companies to identify users and track their online activity across different websites or digital services. This paper presents a first experimental study to detect advertisements and tracker by inspecting fully encrypted network transactions at the TCP/IP network level associated with a website. The first results are encouraging and motivate to extend this first proof-of-concept study even further in the future. A classical application area in the future would be the use in areas where communication can only be accessed on encrypted TCP/IP level (keyword secure IoT environments) or the presented approach is used simply to enable a classical extension of the portfolio for tracker detection.

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Vorheriges Kapitel PointPuff: An Ed25519 Optimization Implementation

Nächstes Kapitel What’s Inside a Node? Malicious IPFS Nodes Under the Magnifying Glass

The source code of our study is available at: https://github.com/wim50594/network-traffic-tracker-observer.

https://majestic.com/reports/majestic-million.

https://easylist.to/.

https://github.com/brave/adblock-rust.

The interested reader can find an in-depth analysis in [22].

https://www.cyren.com/products/url-lookup-api.

Acar, G., Eubank, C., Englehardt, S., Juarez, M., Narayanan, A., Diaz, C.: The web never forgets: persistent tracking mechanisms in the wild. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 674–689 (2014). https://doi.org/10.1145/2660267.2660347

Agogo, D.: Invisible market for online personal data: an examination. Electron. Mark. 31(4), 989–1010 (2020). https://doi.org/10.1007/s12525-020-00437-0CrossRef

Akbari, I., et al.: A look behind the curtain: traffic classification in an increasingly encrypted web. In: Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1 (2021). https://doi.org/10.1145/3447382

Bekos, P., Papadopoulos, P., Markatos, E.P., Kourtellis, N.: The Hitchhiker’s guide to facebook web tracking with invisible pixels and click IDs. In: Proceedings of the ACM Web Conference 2023 (2023).https://doi.org/10.1145/3543507.3583311

Bhagavatula, S., Dunn, C., Kanich, C., Gupta, M., Ziebart, B.: Leveraging machine learning to improve unwanted resource filtering. In: Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop, pp. 95–102 (2014). https://doi.org/10.1145/2666652.2666662

Brave: Fingerprint randomization (2020). https://brave.com/privacy-updates/3-fingerprint-randomization/

Bujlow, T., Carela-Español, V., Solé-Pareta, J., Barlet-Ros, P.: A survey on web tracking: mechanisms, implications, and defenses. Proc. IEEE 105(8), 1476–1510 (2017). https://doi.org/10.1109/JPROC.2016.2637878CrossRef

Chen, Q., Ilia, P., Polychronakis, M., Kapravelos, A.: Cookie swap party: abusing first-party cookies for web tracking. In: Proceedings of the Web Conference 2021, pp. 2117–2129 (2021). https://doi.org/10.1145/3442381.3449837

Demir, N., Theis, D., Urban, T., Pohlmann, N.: Towards understanding CNAME based first-party cookie tracking in the field. In: GI Sicherheit 2022 (2022)

10.

Dimova, Y., Acar, G., Olejnik, L., Joosen, W., Van Goethem, T.: The CNAME of the game: large-scale analysis of DNS-based tracking evasion. Proc. Priv. Enhanc. Technol. 3, 394–412 (2021). https://doi.org/10.2478/popets-2021-0053CrossRef

11.

Englehardt, S., Narayanan, A.: Online tracking: a 1-million-site measurement and analysis. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1388–1401 (2016).https://doi.org/10.1145/2976749.2978313

12.

Fathi-Kazerooni, S., Kaymak, Y., Rojas-Cessa, R.: Tracking user application activity by using machine learning techniques on network traffic. In: 2019 International Conference on Artificial Intelligence in Information and Communication, pp. 405–410 (2019). https://doi.org/10.1109/ICAIIC.2019.8669040

13.

Gugelmann, D., Happe, M., Ager, B., Lenders, V.: An automated approach for complementing ad blockers’ blacklists. In: Proceedings on Privacy Enhancing Technologies, pp. 282–298. No. 2 (2015). https://doi.org/10.1515/popets-2015-0018

14.

Hayes, J., Danezis, G.: K-Fingerprinting: a robust scalable website fingerprinting technique. In: Proceedings of the 25th USENIX Conference on Security Symposium, pp. 1187–1203 (2016)

15.

Iqbal, U., Shafiq, Z., Qian, Z.: The ad wars: retrospective measurement and analysis of anti-adblock filter lists. In: Proceedings of the 2017 Internet Measurement Conference, pp. 171–183 (2017). https://doi.org/10.1145/3131365.3131387

16.

Iqbal, U., Snyder, P., Zhu, S., Livshits, B., Qian, Z., Shafiq, Z.: AdGraph: a graph-based approach to ad and tracker blocking. In: 2020 IEEE Symposium on Security and Privacy, pp. 763–776 (2020). https://doi.org/10.1109/SP40000.2020.00005

17.

Jha, N., Trevisan, M., Vassio, L., Mellia, M.: The internet with privacy policies: measuring the web upon consent. ACM Trans. Web 16(3) (2022). https://doi.org/10.1145/3555352

18.

Ma, Q., Huang, W., Jin, Y., Mao, J.: Encrypted traffic classification based on traffic reconstruction. In: 2021 4th International Conference on Artificial Intelligence and Big Data, pp. 572–576 (2021).https://doi.org/10.1109/ICAIBD51990.2021.9459072

19.

Mayer, J.R., Mitchell, J.C.: Third-party web tracking: policy and technology. In: 2012 IEEE Symposium on Security and Privacy, pp. 413–427 (2012). https://doi.org/10.1109/SP.2012.47

20.

Mohajeri Moghaddam, H., et al.: Watching you watch: the tracking ecosystem of over-the-top TV streaming devices. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 131–147 (2019). https://doi.org/10.1145/3319535.3354198

21.

Panchenko, A., Niessen, L., Zinnen, A., Engel, T.: Website fingerprinting in onion routing based anonymization networks. In: Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society, pp. 103–114 (2011). https://doi.org/10.1145/2046556.2046570

22.

Papadogiannakis, E., Papadopoulos, P., Kourtellis, N., Markatos, E.P.: User tracking in the post-cookie era: how websites bypass GDPR consent to track users. In: Proceedings of the Web Conference 2021, pp. 2130–2141 (2021). https://doi.org/10.1145/3442381.3450056

23.

Roesner, F., Kohno, T., Wetherall, D.: Detecting and defending against third-party tracking on the web. In: Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, pp. 155–168 (2012)

24.

Shuba, A., Markopoulou, A., Shafiq, Z.: NoMoAds: effective and efficient cross-app mobile ad-blocking. Proc. Priv. Enhanc. Technol. (4) (2018)

25.

Siby, S., Iqbal, U., Englehardt, S., Shafiq, Z., Troncoso, C.: WebGraph: capturing advertising and tracking information flows for robust blocking. In: 31st USENIX Security Symposium, pp. 2875–2892 (2022)

26.

Siby, S., Juarez, M., Diaz, C., Vallina-Rodriguez, N., Troncoso, C.: Encrypted DNS \(->\) privacy? A traffic analysis perspective. In: Network and Distributed System Security Symposium (2020)

27.

Sirinam, P., Juarez, M., Imani, M., Wright, M.: Deep fingerprinting: undermining website fingerprinting defenses with deep learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2018). https://doi.org/10.1145/3243734.3243768

28.

Sjösten, A., Snyder, P., Pastor, A., Papadopoulos, P., Livshits, B.: Filter list generation for underserved regions. In: Proceedings of The Web Conference 2020, pp. 1682–1692 (2020). https://doi.org/10.1145/3366423.3380239

29.

Snyder, P., Vastel, A., Livshits, B.: Who filters the filters: understanding the growth, usefulness and efficiency of crowdsourced ad blocking. SIGMETRICS (2020). https://doi.org/10.1145/3392144CrossRef

30.

Varmarken, J., Le, H., Shuba, A., Markopoulou, A., Shafiq, Z.: The TV is smart and full of trackers: measuring smart TV advertising and tracking. Proc. Priv. Enhanc. Technol. 2, 129–154 (2020). https://doi.org/10.2478/popets-2020-0021CrossRef

31.

Yuan, S., Abidin, A.Z., Sloan, M., Wang, J.: Internet advertising: an interplay among advertisers, online publishers, ad exchanges and web users. arXiv: 1206.1754 (2012)

32.

Zuboff, S.: The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs (2018)

Titel: Detecting Web Tracking at the Network Layer
verfasst von: Maximilian Wittig
Doğan Kesdoğan
Verlag: Springer Nature Switzerland
Buch: ICT Systems Security and Privacy Protection
Print ISBN: 978-3-031-56325-6

Electronic ISBN: 978-3-031-56326-3

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56326-3_10

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