Forensic-Assisted Structural Self-Reconfiguring Firewall

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@article{1714875,
      author = {Omkar Sahebrao Kedari},
      title = {Forensic-Assisted Structural Self-Reconfiguring Firewall},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {831-839},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714875.pdf},
      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.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1714875}
  }