Agent Web模型--用于强化学习的Web黑客建模
The Agent Web Model -- Modelling web hacking for reinforcement learning
摘要：
网站黑客是恶意行为者用来获取机密信息、修改网页完整性或使网站不可用的常见攻击类型。攻击者使用的工具正变得越来越自动化和复杂，恶意机器学习代理似乎是这一行的下一个发展。为了提供类似的工具，道德黑客，并了解人工代理的影响和局限性，我们在本文中提出了一个模型，形式化网络黑客任务的强化学习代理。我们的模型，名为代理Web模型，认为网络黑客作为一个捕获的旗帜风格的挑战，它定义了强化学习问题在七个不同的抽象层次。我们讨论了这些问题的复杂性方面的行动和国家的代理必须处理，我们表明，这样的模型可以代表大多数相关的Web漏洞。意识到强化学习进步的驱动力是标准化挑战的可用性，我们提供了前三个抽象层的实现，希望社区能够考虑这些挑战，以开发智能网络黑客代理。
英文摘要：
Website hacking is a frequent attack type used by malicious actors to obtain confidential information, modify the integrity of web pages or make websites unavailable. The tools used by attackers are becoming more and more automated and sophisticated, and malicious machine learning agents seems to be the next development in this line. In order to provide ethical hackers with similar tools, and to understand the impact and the limitations of artificial agents, we present in this paper a model that formalizes web hacking tasks for reinforcement learning agents. Our model, named Agent Web Model, considers web hacking as a capture-the-flag style challenge, and it defines reinforcement learning problems at seven different levels of abstraction. We discuss the complexity of these problems in terms of actions and states an agent has to deal with, and we show that such a model allows to represent most of the relevant web vulnerabilities. Aware that the driver of advances in reinforcement learning is the availability of standardized challenges, we provide an implementation for the first three abstraction layers, in the hope that the community would consider these challenges in order to develop intelligent web hacking agents.
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。
