AI-Powered Dynamic Web Filtering for Encrypted Traffic

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AI-Powered Dynamic Web Filtering for Encrypted Traffic

Karan Nagwani

Prasanth G, Prakhar Pratyush jaiswal

Computer Science and Engineering

Jain University

Bangalore, India nagwani89karan1@gmail.com

Abstract— The exponential growth of encrypted web traffic through SSL/TLS protocols poses new challenges for traditional web filtering systems. Conventional methods like blacklist filtering, keyword blocking, and static content analysis are increasingly ineffective against encrypted traffic. This research paper proposes an AI-powered dynamic web filtering framework for encrypted traffic, leveraging machine learning, behavioral analysis, and traffic metadata inspection to identify harmful or inappropriate content while preserving user privacy. Previous research in traditional filtering techniques and modern solutions is referenced to support the proposed methodology.

Keywords: AI Web Filtering, Encrypted Traffic, SSL/TLS Inspection, Machine Learning, Privacy-Preserving Filtering, Cybersecurity

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