Enhancing Security and Resilience of Browser Fingerprinting Models Against Adversarial Attacks: A Deep Learning Approach

Abstract

Browser fingerprinting has emerged as a critical technique for user identification, fraud detection, device authentication, and cybersecurity monitoring. Recent advances in deep learning have significantly improved the accuracy of browser fingerprinting systems by enabling automated feature extraction and complex pattern recognition across browser and device attributes. However, the increasing sophistication of adversarial machine learning techniques has exposed vulnerabilities in these systems, allowing attackers to manipulate browser attributes and evade identification mechanisms. This study examines the security and resilience challenges associated with deep learning-based browser fingerprinting models under adversarial conditions. Drawing upon contemporary research in fairness-aware machine learning, representation learning, optimization, and robustness, the paper proposes a resilience-oriented framework that integrates adversarial training, representation neutralization, distribution matching, and robust optimization strategies. The study synthesizes theoretical foundations from machine learning robustness literature and applies them to browser fingerprinting environments. Findings indicate that resilient fingerprinting systems require a balance between predictive performance, robustness, fairness, and adaptability. The proposed framework demonstrates how deep learning architectures can be strengthened against evasion attacks while maintaining identification accuracy and operational reliability. The research contributes a comprehensive perspective on securing browser fingerprinting models in increasingly hostile digital environments.