游戏资源本地化攻击的统计努力建模
Statistical Effort Modelling of Game Resource Localisation Attacks
AI总结：
本文提出了一种完整的方法，用于构建游戏资源本地化攻击的统计努力模型，验证了该方法的可行性，并为软件保护工具用户提供了决策支持。
摘要：
关于Man-At-The-End（MATE）软件保护措施有效性的证据，主要来自于有限的实证研究。最近，一种可自动化的办法被提出，以获取攻击（受保护）软件所需努力的统计模型。所提出的方法被概述用于多种攻击策略，但未被实例化、评估或验证那些需要与受攻击软件进行人机交互的策略。在本文中，我们提出了该方法的完整实例化，以获取游戏资源本地化攻击的统计努力模型，这标志着向创建游戏作弊迈出了重要一步，这是MATE攻击的典型例子。我们详细讨论了实例化的所有相关方面以及两个游戏使用案例所获得的结果。我们的结果确认了所提出方法的可行性及其对软件保护工具用户决策支持的用途。这些结果为获取软件保护对逆向工程攻击影响的模型开辟了新途径，这将比涉及人类参与的实证研究有更大的扩展性。
英文摘要：
Evidence on the effectiveness of Man-At-The-End (MATE) software protections, such as code obfuscation, has mainly come from limited empirical research. Recently, however, an automatable method was proposed to obtain statistical models of the required effort to attack (protected) software. The proposed method was sketched for a number of attack strategies but not instantiated, evaluated, or validated for those that require human interaction with the attacked software. In this paper, we present a full instantiation of the method to obtain statistical effort models for game resource localisation attacks, which represent a major step towards creating game cheats, a prime example of MATE attacks. We discuss in detail all relevant aspects of our instantiation and the results obtained for two game use cases. Our results confirm the feasibility of the proposed method and its utility for decision support for users of software protection tools. These results open up a new avenue for obtaining models of the impact of software protections on reverse engineering attacks, which will scale much better than empirical research involving human participants.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
