Abstract
Traditional firewall architectures rely on static rule-based enforcement and fixed network segmentation, resulting in structural predictability and repeated bypass risks. This paper presents a Forensic-Assisted Structural Self-Reconfiguring Firewall architecture that enables real-time structural mutation governed by risk thresholds rather than conventional rule updates. The proposed framework integrates an AI-driven forensic reconstruction engine, predictive cyber twin simulation, and a blockchain-based structural intelligence ledger for validated containment retrieval. A structural decision engine dynamically modifies segmentation topology, trust boundaries, and routing paths during live threat conditions. To preserve operational continuity, a Transitional Flow Identity (TFI) mechanism ensures transactional dual-configuration switching with zero packet loss and uninterrupted session maintenance. The architecture introduces a self-learning, structurally adaptive firewall model designed to eliminate architectural predictability while maintaining system stability and continuous containment intelligence evolution.
Keywords
Structural Firewall Reconfiguration, AI-Powered Forensic Analysis, AI Cyber Twins, Adaptive Cyber Defense, Blockchain Security Ledger, Moving Target Defense, Zero-Trust Architecture.
Citations
IRE Journals:
Omkar Sahebrao Kedari "Forensic-Assisted Structural Self-Reconfiguring Firewall" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 831-839 https://doi.org/10.64388/IREV9I9-1714875
IEEE:
Omkar Sahebrao Kedari
"Forensic-Assisted Structural Self-Reconfiguring Firewall" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1714875
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