论文标题
代理Web模型 - 建模用于强化学习的Web黑客攻击
The Agent Web Model -- Modelling web hacking for reinforcement learning
论文作者
论文摘要
网站黑客是恶意参与者使用的常见攻击类型，以获取机密信息，修改网页的完整性或使网站不可用。攻击者使用的工具正在变得越来越自动化和复杂，恶意的机器学习代理似乎是这一行的下一个发展。为了为道德黑客提供类似的工具，并了解人造代理的影响和局限性，我们在本文中介绍了一种模型，该模型正式化了用于加强学习剂的网络黑客攻击任务。我们的模型名为Agent Web模型，将Web入侵视为一种捕获风格的挑战，并在七个不同级别的抽象级别定义了强化学习问题。我们讨论了这些问题的复杂性，并指出代理必须处理的问题，我们表明，这种模型允许代表大多数相关的网络漏洞。意识到，加强学习进步的驱动力是标准化挑战的可用性，我们为前三个抽象层提供了实施，希望社区能够考虑这些挑战以开发智能的网络黑客入侵代理。
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.
