Problem
Research questions and friction points this paper is trying to address.
cyber operation simulation
automated agents
attack and defense
The MAL Simulator: Cyber Operations Simulation based on Attack & Defense Graphs
🤖 AI Summary
开发了基于元攻击语言的MAL模拟器，用于网络攻防模拟和系统分析，并通过案例研究训练了攻防自动化代理。
📝 Abstract
We have developed the MAL Simulator, a cyber operation simulator based on the Meta Attack Language (MAL). The MAL Simulator is intended for decision-driven cyber attack and defense simulations, for system analysis and the development of automated agents. By building the simulator around an attack modeling language, it can be adapted to different target domains without modifying the source code. We used the simulator for two case studies where we trained two types of agents for automated cyber operations: a defensive agent and an offensive agent. To ground the experiments, we base the models in data collected from an emulated network implemented in the cyber range CRATE. We found that the trained attacker policy could reach the designated targets more efficiently than the compared search methods, and that the trained defender agent induced lower costs than a naive heuristic agent under noisy alert conditions. When testing the RL attacker against the RL defender, we found that the performance of the defenders dropped significantly. This emphasizes the importance of cyber attack simulators to facilitate training both offensive and defensive agents. The MAL Simulator and associated tooling is publicly available and provides common interfaces for compatibility with existing machine learning frameworks.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Meta Attack Language
cyber operation simulator
automated agents
reinforcement learning
attack and defense simulation
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