Generative Adversarial Network (GAN) Modeling for CNI Vulnerability Discovery: An Immutable Knowledge Base for Automated Infrastructure Hardening via Distributed Ledger Technology

Abstract

As Critical National Infrastructure (CNI) becomes increasingly digitized, traditional reactive security assessments are failing to keep pace with automated threats. This paper proposes an autonomous framework integrating Generative Adversarial Networks (GANs) and Distributed Ledger Technology (DLT) for proactive vulnerability discovery and immutable infrastructure hardening. We utilize a Physics-Aware Wasserstein GAN to synthesize protocol-specific attack vectors that identify "zero-day" weaknesses in Industrial Control Systems (ICS) by exploring the operational state space of protocols like DNP3 and Modbus. Discovered vulnerabilities are committed to an Immutable Knowledge Base (IKB) on a sharded, permissioned blockchain, providing a "Single Source of Truth" for cross-sector threat intelligence. To automate mitigation, Smart Contracts orchestrate infrastructure hardening by validating patches through digital twin simulations before network-wide deployment. Experimental results using HELICS and NS-3 demonstrate that this architecture reduces the Mean Time to Remediate (MTTR) from days to sub-second intervals. Finally, we address long-term security by incorporating Post-Quantum Cryptography (PQC) to protect the ledger against emerging quantum threats.