Cross‑validation yielded 94 % accuracy, which we then applied to the full corpus to estimate the model contribution.
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While combining models increases apparent entropy, the reduces the effective search space for attackers. The generator re‑uses dictionary entries as seeds for the Markov and neural components, creating deterministic bridges that can be learned by a machine‑learning attacker. Cross‑validation yielded 94 % accuracy, which we then
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The (which adds personal‑data tokens like birthdays) contributed to the highest targeted cracking success when the attacker possessed auxiliary personal information.