自主网络防御的网络环境设计
Network Environment Design for Autonomous Cyberdefense
摘要：
强化学习（RL）已被证明适用于开发具有人类水平性能的复杂游戏代理。然而，目前还不清楚如何有效地使用RL来执行网络安全任务。为了发展这样的理解，有必要使用仿真和模拟系统开发RL代理，使研究人员能够对广泛的现实威胁和网络条件进行建模。证明特定的RL算法在某些条件下可以有效地防御网络，可能不一定能在威胁，网络条件和安全目标发生变化时了解算法的性能。本文介绍了一种新的网络环境设计方法和一个软件框架，以解决网络防御不能被定义为一个简单的一组固定规则的单一游戏的根本问题。我们展示了我们的方法是必要的，以促进RL网络防御者的发展，这些防御者对针对代理学习的攻击具有鲁棒性。我们的框架可以开发和模拟具有复杂行为的对手，包括对RL网络防御者的中毒和逃避攻击。
英文摘要：
Reinforcement learning (RL) has been demonstrated suitable to develop agents that play complex games with human-level performance. However, it is not understood how to effectively use RL to perform cybersecurity tasks. To develop such understanding, it is necessary to develop RL agents using simulation and emulation systems allowing researchers to model a broad class of realistic threats and network conditions. Demonstrating that a specific RL algorithm can be effective for defending a network under certain conditions may not necessarily give insight about the performance of the algorithm when the threats, network conditions, and security goals change. This paper introduces a novel approach for network environment design and a software framework to address the fundamental problem that network defense cannot be defined as a single game with a simple set of fixed rules. We show how our approach is necessary to facilitate the development of RL network defenders that are robust against attacks aimed at the agent's learning. Our framework enables the development and simulation of adversaries with sophisticated behavior that includes poisoning and evasion attacks on RL network defenders.
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。
