This work studies no-regret online learning in Repeated Stackelberg Security Games with time-varying attack intensities. We formulate an extended security game in which an attacker may select multiple targets and derive an exact mixed-integer linear programming oracle under a optimistic tie-breaking rule. Under full-information feedback, the oracle is integrated with Follow-the-Perturbed-Leader and yields expected $\mathcal{O}(\sqrt{T})$ regret against non-anticipating sequences with time-varying follower numbers, attack intensities, and attacker types. Under bandit feedback, we consider multiple followers sharing a fixed attacker type and use a barycentric-spanner construction to reconstruct utility estimates from aggregate attack observations, obtaining expected $\mathcal{O}(T^{2/3})$ regret. Extensive simulations demonstrate the robustness and effectiveness of our approach under full and partial information feedback.

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