A Reproducible Simulation Framework Coupling RLWE Key Exchange with Sequential Bayesian and Dynamic Bayesian Network Threat Inference: Sensitivity, Misspecification, and Falsifiability Analysis

We present a fully reproducible, simulation based framework that couples Ring Learning With Errors (RLWE) lattice based key exchange with sequential Bayesian threat inference, extended by a dynamic Bayesian network (DBN) for temporal attack phase modeling. The contribution is methodological rather than a new cryptographic primitive: (i) a symbolically verified RLWE reconciliation error bound, quantitatively grounded against real ML KEM (FIPS 203) noise parameters; (ii) a sequential Bayesian detector with a proved almost sure convergence guarantee; (iii) a single seeded Python pipeline that generates every number in this paper, including a from scratch, correctness verified O(n log n) Number Theoretic Transform implementation, matching ML KEM's exact algebraic structure and verified bit for bit against schoolbook convolution, with wall clock measured, not analytical, timings; (iv) multi parameter Sobol' and Morris sensitivity analysis with bootstrap confidence intervals, confirmed converged at N equals 8192 Saltelli samples; (v) an explicit model misspecification study in which the detector is evaluated under a Student t data generating process it was not designed for, alongside a likelihood ratio test showing the first order Markov attack phase assumption is measurably outperformed by a second order alternative on synthetic multi stage sequences; (vi) a two feature extension compared against logistic regression and isolation forest baselines, with pre and post isotonic calibration, and a from scratch Monte Carlo Dropout Bayesian neural network, trained, not merely specified, and evaluated for both discrimination and calibration; and (vii) explicit falsifiability conditions and a staged roadmap, now naming concrete public datasets, toward hardware and real channel validation. Headline results are reported as computed: single feature detector AUC equal to 0.874, rising to 0.890 to 0.908 with a second feature (logistic regression and trained BNN respectively) and falling to 0.814 under heavy tailed (Student t, degrees of freedom equal to 3) misspecification; the first order Markov assumption is rejected in favor of a second order model at p less than 10 to the negative 6 on synthetic multi stage traces; the verified NTT implementation is 6.24 times faster than schoolbook convolution at ML KEM's actual ring dimension, on identical unoptimized Python hardware. Comparative analysis against NIST standardized schemes (CRYSTALS Kyber, CRYSTALS Dilithium, Falcon), NTRU, and Classic McEliece uses each scheme's own published specifications; a two order of magnitude gap remains between our verified but unoptimized NTT and Kyber's audited, hardware benchmarked implementation, now attributable to implementation optimization rather than algorithmic complexity class, and not a claim of competitiveness.