1. bookVolume 10 (2020): Edition 4 (October 2020)
Détails du magazine
License
Format
Magazine
eISSN
2449-6499
Première parution
30 Dec 2014
Périodicité
4 fois par an
Langues
Anglais
Accès libre

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

Publié en ligne: 15 Jun 2020
Volume & Edition: Volume 10 (2020) - Edition 4 (October 2020)
Pages: 243 - 253
Reçu: 14 Oct 2019
Accepté: 29 Apr 2020
Détails du magazine
License
Format
Magazine
eISSN
2449-6499
Première parution
30 Dec 2014
Périodicité
4 fois par an
Langues
Anglais

[1] Kristol D.M., HTTP cookies: Standards, privacy, and politics, ACM Trans. Internet Techn. 1 (2) (2001) 151–198.Search in Google Scholar

[2] Low C., Cookie law explained, 2016. on-line https://www.cookielaw.org/the-cookie-law/ (retrieved:03/2020).Search in Google Scholar

[3] Alaca, F., Van Oorschot, P. C. (2016, December). Device fingerprinting for augmenting web authentication: classification and analysis of methods. In Proceedings of the 32nd Annual Conference on Computer Security Applications (pp. 289-301).10.1145/2991079.2991091Search in Google Scholar

[4] Nagaraja, S., Shah, R. (2019, May). Clicktok: click fraud detection using traffic analysis. In Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks (pp. 105-116).10.1145/3317549.3323407Search in Google Scholar

[5] Mouawi, R., Elhajj, I.H., Chehab, A. et al. Crowd-sourcing for click fraud detection. EURASIP J. on Info. Security 2019, 11 (2019)10.1186/s13635-019-0095-1Search in Google Scholar

[6] Dave, V., Guha, S., Zhang, Y. (2012, August). Measuring and fingerprinting click-spam in ad networks. In Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication (pp. 175-186).10.1145/2377677.2377715Search in Google Scholar

[7] Vastel, A., Rudametkin, W., Rouvoy, R., Blanc, X. (2020, February). FP-Crawlers: Studying the Resilience of Browser Fingerprinting to Block Crawlers. In NDSS Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb’20).10.14722/madweb.2020.23010Search in Google Scholar

[8] 2019. https://www.emarketer.com/content/digital-ad-fraud-2019Search in Google Scholar

[9] Barker S., “Future Digital Advertising, Artificial Intelligence & Advertising Fraud 2019-2023”, Juniper Research, 2019Search in Google Scholar

[10] Eckersley P., How unique is your web browser? in: Privacy Enhancing Technologies, 10th International Symposium, PETS 2010, Berlin, Germany, July 21-23, 2010. Proceedings, 2010, pp. 1–1810.1007/978-3-642-14527-8_1Search in Google Scholar

[11] Laperdrix, P., Bielova, N., Baudry, B., Avoine, G. (2019). Browser Fingerprinting: A survey. arXiv preprint arXiv:1905.01051.Search in Google Scholar

[12] Kobusinska, A., Pawluczuk, K., Brzezinski, J. (2018). Big Data fingerprinting information analytics for sustainability. Future Generation Computer Systems, 86, 1321-1337.10.1016/j.future.2017.12.061Search in Google Scholar

[13] Mayer J R. 2009. Any person... a pamphleteer”: Internet Anonymity in the Age of Web 2.0. Undergraduate Senior Thesis, Princeton University (2009).Search in Google Scholar

[14] Steven E. and Arvind N. 2016. Online Tracking: A 1-million-site Measurement and Analysis. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS ’16). ACM, New York, NY, USA, 1388–1401.Search in Google Scholar

[15] Gómez-Boix, A., Laperdrix, P., Baudry, B. (2018, April). Hiding in the crowd: an analysis of the effectiveness of browser fingerprinting at large scale. In Proceedings of the 2018 world wide web conference (pp. 309-318).10.1145/3178876.3186097Search in Google Scholar

[16] Cao, Y., Li, S., Wijmans, E. (2017, March). (Cross-) Browser Fingerprinting via OS and Hardware Level Features. In NDSS.10.14722/ndss.2017.23152Search in Google Scholar

[17] 2020. The Evolution of Hi-Def Fingerprinting in Bot Mitigation - Distil Networks. https://resources.distilnetworks.com/all-blog-posts/device-fingerprinting-solution-bot-mitigationSearch in Google Scholar

[18] 2020. Device Tracking Add-on for minFraud Services - MaxMind. https://dev.maxmind.com/minfraud/device/Search in Google Scholar

[19] Bursztein, E., Malyshev, A., Pietraszek, T., Thomas, K. (2016, October). Picasso: Lightweight device class fingerprinting for web clients. In Proceedings of the 6th Workshop on Security and Privacy in Smartphones and Mobile Devices (pp. 93-102).10.1145/2994459.2994467Search in Google Scholar

[20] Renjith, S. (2018). Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approach. arXiv preprint arXiv:1805.00464.Search in Google Scholar

[21] Zhang, X., Han, Y., Xu, W., Wang, Q. (2019). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information Sciences.Search in Google Scholar

[22] Ludwig, S. A. (2019). Applying a neural network ensemble to intrusion detection. Journal of Artificial Intelligence and Soft Computing Research, 9(3), 177-188.10.2478/jaiscr-2019-0002Search in Google Scholar

[23] de Souza, G. B., da Silva Santos, D. F., Pires, R. G., Marana, A. N., Papa, J. P. (2019). Deep features extraction for robust fingerprint spoofing attack detection. Journal of Artificial Intelligence and Soft Computing Research, 9(1), 41-49.10.2478/jaiscr-2018-0023Search in Google Scholar

[24] Salakhutdinov, R., Hinton, G. (2009). Semantic hashing. International Journal of Approximate Reasoning, 50(7), 969-978.10.1016/j.ijar.2008.11.006Search in Google Scholar

[25] 2020. FingerprintJS. Fraud detection API. https://fingerprintjs.com/Search in Google Scholar

[26] Leskovec J., Rajaraman A., Ullman J.D.: Mining of Massive Datasets, Cambridge University Press, 201410.1017/CBO9781139924801Search in Google Scholar

[27] Azgomi, H., Mahjur, A. (2013). A Solution for Calculating the False Positive and False Negative in LSH Method to Find Similar Documents. Journal of Basic and Applied Research, 3, 466-472.Search in Google Scholar

[28] Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. MIT PressSearch in Google Scholar

[29] Bengio Y., Learning deep architectures for ai Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1–127, Jan. 2009.10.1561/2200000006Search in Google Scholar

[30] Olson, D.L., Delen, D.: Advanced Data Mining Techniques, 1st edn. Springer, Heidelberg (2008).Search in Google Scholar

Articles recommandés par Trend MD

Planifiez votre conférence à distance avec Sciendo