Architectural evolution and surveillance vulnerability mitigation in modern web browsers: Predictive analytics and anomaly detection approaches
Modern web browsers have evolved from simple document viewers into complex, security-critical platforms mediating billions of daily user interactions, making them prime targets for sophisticated surveillance attacks. This study investigates how the architectural evolution of web browsers — across four generations from monolithic single-process (2000–2005) to site-isolated multi-process architectures (2019–2025) — affects the detectability and mitigation of surveillance vulnerabilities. A mixed-methods design combined systematic literature review, quantitative architectural security scoring, construction of a longitudinal vulnerability dataset drawn exclusively from public CVE/NVD records and vendor security bulletins (623 vulnerabilities tracked across 2025), and empirical evaluation of five detection methodologies: rule-based, statistical analysis, machine learning (Random Forest, SVM, Gradient Boosting), deep learning (CNN and two-layer LSTM), and ensemble methods. Architectural analysis revealed progressive security improvements, with site-isolated architectures achieving composite security and privacy scores of 91 and 89 respectively, compared to 45 and 38 for monolithic designs, at a 20% performance cost. Ensemble detection methods achieved the highest accuracy (95–98%) across all six surveillance threat categories (Tracking Cookies, Fingerprinting, Data Exfiltration, Session Hijacking, XSS, CSRF), with deep learning achieving 90–95%. Temporal analysis of the vulnerability dataset demonstrated a 91.6% mitigation success rate and an average detection-to-mitigation window of 8.3 days. These results establish a quantitative link between architectural generation and vulnerability detectability, providing actionable guidance for browser vendors integrating continuous security monitoring, enterprise security teams deploying ML-based browser telemetry analysis, and forensic laboratories conducting post-incident browser surveillance investigations.