This work aims to simplify and evaluate resource allocation strategies in wildlife security. The current standard, the Stackelberg Security Game (SSG), has limitations due to invalid assumptions and high computational demands for large action sets. First, a simplified game model is proposed where the rangers act as followers, allowing the use of observed attack data to infer poachers' mixed-strategy through point pattern analysis and spatial features. This helps rangers find better mixed-strategy responses than the SSG. Secondly, an evaluation framework for wildlife security games is presented, which simulates wildlife, ranger, and poacher movements using real-world geography to assess strategy effectiveness. It contrasts rangers' strategies with random movements and intelligent poacher models that adapt based on past events. The results show that rangers perform better with the Stackelberg model, especially when acting as followers. This approach outperforms baseline models and Nash equilibrium, particularly when arrests are a key utility factor. Future work should focus on applying these methods to real-world data, incorporating real-time updates, satellite imagery, and modeling multiple groups to refine the evaluation framework.