Summary of Designing Robust Cyber-defense Agents with Evolving Behavior Trees, by Nicholas Potteiger et al.
Designing Robust Cyber-Defense Agents with Evolving Behavior Trees
by Nicholas Potteiger, Ankita Samaddar, Hunter Bergstrom, Xenofon Koutsoukos
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an approach to design autonomous cyber-defense agents using behavior trees with learning-enabled components, referred to as Evolving Behavior Trees (EBTs). The goal is to create robust and trustworthy agents that can execute complex long-term defense tasks in a reactive manner. To achieve this, the authors develop a novel abstract cyber environment for learning the structure of an EBT and optimize learning-enabled components for deployment. The learned EBT structure is evaluated in a simulated cyber environment, demonstrating its effectiveness in mitigating threats and enhancing network visibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to create smart agents that can defend computer networks from cyber-attacks. These agents use decision-making rules called behavior trees and learn how to make better decisions over time. The authors created a special environment where the agent can practice defending against different types of attacks. They found that their approach is effective in keeping the network safe and provides explanations for its actions. |
