Open Access

Robust Browser Fingerprinting Under Adversarial Conditions: An AI-Driven Detection and Defense Architecture

https://doi.org/10.55640/ijaair-v03i09-07

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Nimal Perera ¹ , Anjali Fernando ¹ ,

⁴ Department of Computer Engineering and Machine Intelligenc University of Colombo, Colombo, Sri Lanka

⁴ Faculty of Computing and Artificial Intelligence University of Moratuwa, Moratuwa, Sri Lanka

Abstract

Browser fingerprinting has emerged as a critical technique for device identification, fraud detection, behavioral analytics, and cybersecurity monitoring. However, the increasing availability of anti-fingerprinting tools, browser spoofing frameworks, privacy-enhancing extensions, and adversarial machine learning techniques has significantly reduced the reliability of conventional browser fingerprinting systems. Under adversarial conditions, attackers manipulate browser attributes, inject synthetic behaviors, and employ evasion mechanisms to bypass detection models. This paper proposes an AI-driven detection and defense architecture designed to enhance the robustness of browser fingerprinting against adversarial manipulation. The study synthesizes concepts from artificial intelligence, edge intelligence, decentralized computing, data science, and complex systems engineering to develop a multi-layer adaptive framework capable of detecting anomalous fingerprint behaviors and adversarial perturbations. The architecture integrates feature integrity analysis, adversarial anomaly detection, behavioral correlation learning, decentralized verification mechanisms, and edge-based intelligence processing. Findings indicate that AI-enabled adaptive fingerprinting provides improved resilience against spoofing attacks, fingerprint randomization, and identity obfuscation techniques. The proposed framework contributes to the development of trustworthy browser identification systems capable of operating effectively in increasingly hostile digital environments.

Keywords

Browser Fingerprinting, Adversarial Machine Learning, Artificial Intelligence, Cybersecurity

References

D. Angelo and M. Salzer, “Identification of token contracts on Ethereum: Standard compliance and beyond,” Int. J. Data Sci. Anal., pp. 1–20, 2021.

M. N. M. Bhutta, “A survey on blockchain technology: Evolution, architecture and security,” IEEE Access, vol. 9, pp. 61048–61073, 2021.

L. Cao, “A new age of AI: Features and futures,” IEEE Intell. Syst., vol. 37, no. 1, pp. 25–37, Jan./Feb. 2022.

L. Cao, “Coupling learning of complex interactions,” J. Inf. Process. Manage., vol. 51, no. 2, pp. 167–186, 2015.

L. Cao, Data Science Thinking: The Next Scientific, Technological and Economic Revolution. Berlin, Germany : Springer, 2018.

L. Cao, Metasynthetic Computing and Engineering of Complex Systems. London, U.K. : Springer, 2015.

S. Hamburg, “Call to join the decentralized science movement,” Nature, vol. 600, p. 221, 2021.

L. Lee, “All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda,” 2021, arXiv:2110.05352.

Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surv. Tut., vol. 19, no. 4, pp. 2322–2358, Oct.–Dec. 2017.

M. Nasir, “Scalable blockchains—A systematic review,” Future Gener. Comput. Syst., vol. 126, pp. 136–162, 2022.

United Nations Development Programme, “Decentralization: A sampling of definitions,” UNDP, New York, NY, USA, Working Paper, Oct. 1999.

D. Xu, “Edge intelligence: Empowering intelligence to the edge of network,” Proc. IEEE, vol. 109, no. 11, pp. 1778–1837, Nov. 2021.

Y. Zhang, Mobile Edge Computing. Berlin, Germany : Springer, 2022, pp. 1–105.

Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, “Edge intelligence: Paving the last mile of artificial intelligence with edge computing,” Proc. IEEE, vol. 107, no. 8, pp. 1738–1762, Aug. 2019.

Ganapathy, S. K. (2025). Beyond Accuracy: Adversarial Robustness of Deep Learning-Based Browser Fingerprinting Systems. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(12), 29–39. https://doi.org/10.64917/feaiml/Volume02Issue12-03

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International Journal of Advanced Artificial Intelligence Research

Robust Browser Fingerprinting Under Adversarial Conditions: An AI-Driven Detection and Defense Architecture

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