Cognitive-Adaptive Multilayer AI Firewall Architecture (C-AMLAFA) for Encrypted Traffic Analysis in Enterprise Networks

The rapid advancement of encrypted network traffic, combined with the potential of quantum computing, poses considerable challenges to current network security frameworks. Firewalls and Intrusion Detection Systems are problematic because the primary strategies are coupled with payload analysis, which is not viable in encrypted traffic, and raises concerns related to privacy. Our proposal is Cognitive-Adaptive Multi-Layer AI Firewall Architecture (C-AMLAFA) and this leads to the preservation of privacy, detection of encrypted traffic, and detection of quantum computing related threats. The framework contains three synergistic layers. The first layer employs the behavioral biometric profiling technique based on temporal flow and uses unobservable discrimination and loss-based discrimination metrics coupled with the temporal flow and adjusted constructs of packet inter-arrival time distributions and packet size thresholds to create behavioral biometric profiles. The second layer applies lattice-based cryptography with the Ring-LWE hardness assumption to obtain features with, provably, quantum-resistance. The third layer uses contextual adaptive communication with online learning parameters to real-time threat intelligence and the formal regret construct. Out of the 15 enterprise network environments, over millions of encrypted connections achieved more than a 6-month time frame, C-AMLAFA achieved 94.7% detection accuracy. Compared to the other three, the traditional, machine learning, and deep learning models, C-AMLAFA showed improvement on all parameters. The proposed model also displays resilience to quantum-assisted attacks, behavioral mimicry, and computing shift evasion with a balanced complexity. The results validate the proposed architecture for next-generation secure networks.