Summary of Machine Theory Of Mind For Autonomous Cyber-defence, by Luke Swaby et al.
Machine Theory of Mind for Autonomous Cyber-Defence
by Luke Swaby, Matthew Stewart, Daniel Harrold, Chris Willis, Gregory Palmer
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 proposed Graph-In, Graph-Out (GIGO)-ToM architecture, which is based on graph neural networks (GNNs), offers a novel Theory of Mind (ToM) approach for predicting the goals, behaviors, and contextual beliefs of autonomous cyber agents. This ToM method can accurately predict both the targets and attack trajectories of adversarial cyber agents over arbitrary computer network topologies. The GIGO-ToM architecture is compared to a Graph-In, Dense-Out (GIDO)-ToM architecture in an abstract cyber-defence environment, with empirical evaluations showing that GIGO-ToM can effectively characterize the policies of various unseen cyber-attacking agents across different network topologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to understand and predict the behavior of computer hackers. It uses special kinds of artificial intelligence called graph neural networks (GNNs) to learn about the goals and motivations of hackers. This approach is called Theory of Mind, or ToM. The researchers created a new kind of GNN-based ToM architecture that can predict what hackers will do next on different types of computer networks. They tested this approach against another type of ToM model and found that it worked well. |
Keywords
» Artificial intelligence » Gnn
