Summary of Out-of-distribution Detection For Neurosymbolic Autonomous Cyber Agents, by Ankita Samaddar et al.
Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents
by Ankita Samaddar, Nicholas Potteiger, Xenofon Koutsoukos
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 out-of-distribution (OOD) monitoring algorithm to detect anomalous situations in reinforcement learning-based autonomous agents for cyber applications. The OOD monitoring algorithm uses a Probabilistic Neural Network (PNN) to identify OOD situations with discrete states and actions. The approach is integrated with a neurosymbolic autonomous cyber agent that combines behavior trees with learning-enabled components. Experimental results demonstrate the efficiency of the proposed approach in a simulated cyber environment under different adversarial strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates an intelligent agent for cybersecurity using reinforcement learning (RL) algorithms. This agent can learn and adapt to new situations, but it might not always be trustworthy. To fix this, the researchers developed an algorithm that detects when the agent is in a situation it’s not prepared for. They tested this approach by combining it with a neurosymbolic autonomous cyber agent and running simulations under different scenarios. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning
