References
- [1] Kristol D.M., HTTP cookies: Standards, privacy, and politics, ACM Trans. Internet Techn. 1 (2) (2001) 151–198.
- [2] Low C., Cookie law explained, 2016. on-line https://www.cookielaw.org/the-cookie-law/ (retrieved:03/2020).
- [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).10.1145/2991079.2991091
- [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).10.1145/3317549.3323407
- [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/s13635-019-0095-1
- [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).10.1145/2377677.2377715
- [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.2020.23010
- [8] 2019. https://www.emarketer.com/content/digital-ad-fraud-2019
- [9] Barker S., “Future Digital Advertising, Artificial Intelligence & Advertising Fraud 2019-2023”, Juniper Research, 2019
- [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_1
- [11] Laperdrix, P., Bielova, N., Baudry, B., Avoine, G. (2019). Browser Fingerprinting: A survey. arXiv preprint arXiv:1905.01051.
- [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.2017.12.061
- [13] Mayer J R. 2009. Any person... a pamphleteer”: Internet Anonymity in the Age of Web 2.0. Undergraduate Senior Thesis, Princeton University (2009).
- [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.
- [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).10.1145/3178876.3186097
- [16] Cao, Y., Li, S., Wijmans, E. (2017, March). (Cross-) Browser Fingerprinting via OS and Hardware Level Features. In NDSS.10.14722/ndss.2017.23152
- [17] 2020. The Evolution of Hi-Def Fingerprinting in Bot Mitigation - Distil Networks. https://resources.distilnetworks.com/all-blog-posts/device-fingerprinting-solution-bot-mitigation
- [18] 2020. Device Tracking Add-on for minFraud Services - MaxMind. https://dev.maxmind.com/minfraud/device/
- [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).10.1145/2994459.2994467
- [20] Renjith, S. (2018). Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approach. arXiv preprint arXiv:1805.00464.
- [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.
- [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-2019-0002
- [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-2018-0023
- [24] Salakhutdinov, R., Hinton, G. (2009). Semantic hashing. International Journal of Approximate Reasoning, 50(7), 969-978.10.1016/j.ijar.2008.11.006
- [25] 2020. FingerprintJS. Fraud detection API. https://fingerprintjs.com/
- [26] Leskovec J., Rajaraman A., Ullman J.D.: Mining of Massive Datasets, Cambridge University Press, 201410.1017/CBO9781139924801
- [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.
- [28] Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. MIT Press
- [29] Bengio Y., Learning deep architectures for ai Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1–127, Jan. 2009.10.1561/2200000006
- [30] Olson, D.L., Delen, D.: Advanced Data Mining Techniques, 1st edn. Springer, Heidelberg (2008).