Cutting network traffic has become a standard for secure communication, but it also prevents traditional Infiltration system (ID) that depends on the payload inspection. This article suggests an AI-operated structure that uses Context-Free Grammar (CFG) on the encrypted network package metadata, which protect privacy, to organize protocol structures without decrypt. Parasd outputs are converted to deep learning models, especially the constructed Convolutional Neural Network (CNN) function vector and the Recurrent Neural Network (RNN), to identify the asymmetrical patterns to indicate a sign of malicious activity. A hybrid threat detection engine integrates both models to benefit from spatial and temporary dependence on the traffic patterns. Finally, a real -time user interface (UI) is developed for monitoring, logging and reporting of events. The proposed system shows high accuracy in encrypted traffic -threatening detection, which ensures compliance with the privacy rules.
 

Encrypted traffic analysis, Context-free grammar pairing, network metadata, CNN, RNN, threatening, privacy protection ID