Prioritizing Post-Quantum Migration in Synthetic Cryptographic Inventories with Machine Learning and QUBO Optimization - Figueira, Larissa de Oliveira; Duarte, Luiz Otavio; Silva, Rafael Lucas (Anais Estendidos do Simpósio Brasileiro de Cibersegurança (SBSeg); 2026: Anais Estendidos do XXVI Simpósio Brasileiro de CibersegurançaDO - 10.5753/sbseg_estendido.2026.30625, 2026)
Post-quantum cryptography migration requires prioritizing cryptographic assets under budget and dependency constraints. This paper presents a reproducible pipeline combining supervised learning, heuristics, MILP, and QUBO. The evaluation covers three synthetic inventories with 1,609 assets, 1,179 migration candidates, and 1,050 dependencies. Logistic regression was selected for system-grouped priority estimation. Linear MILP achieved the highest predicted priority, closely followed by the priority-to-cost greedy method, while dependency-aware methods produced more coherent portfolios. Adding dependencies to QUBO reduced mean mandatory violations from 10.963 to 7.859 and increased coherence from 0.344 to 0.479, with a small priority decrease. Under the adopted soft objective, soft-dependency MILP achieved the best trade-off and substantially lower runtime. These results show that explicit dependency modeling improves migration coherence and that QUBO provides a flexible representation of budget and dependency interactions.