Abstract:

The rise of encrypted communication has made it harder for network security monitoring to find threats than it ever was before. Modern protocols like TLS 1.3, QUIC, and HTTPS have stronger encryption methods built in, so traditional inspection systems can't find strange or malicious flows without putting user privacy at risk. This paper presents an AI-Based Context-Free Grammar Parsing Framework that integrates formal grammar theory and machine learning to examine encrypted network traffic through metadata and syntactic pattern structures. The system learns the basic rules of how encrypted traffic behaves through adaptive grammar induction, while at the same time, machine learning classifiers like Random Forests, Pragatheesh.S Dept. of Computer Science SRM University Tiruchirappalli, India ps3061@srmist.edu.in landscape. In practice, while encryption preserves user data confidentiality, it also masks traffic patterns from traditional inspection systems. The challenge for network operators now is to reconcile two seemingly orthogonal interests: preserving privacy and detecting anomalies. ML methods have shown some success in analyzing flow-level metadata (e.g., packet sizes and timing sequences, TLS handshake patterns); these systems provide high accuracy but little interpretability, often behaving as black boxes. Support Vector Machines, and Bidirectional LSTMs are used to sort threats. The hybrid model makes decisions clear by linking each prediction to clear CFG rules. This solves the problem of black-box AI systems that are hard to understand. The proposed CFG-AI framework has been tested against five state-of-the-art approaches and has been shown to improve the accuracy of intrusion detection by up to 7%, lower the number of false positives by 20%, and give an Explainability Index of 97%, which means that most decisions can be logically traced back.