mlsanityData to 14 Oct 2026

Informing Autonomous Deception Systems with Cyber Expert Performance Data

arXiv 2109.00066 ↗First posted 31 Aug 2021cs.CR cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

The performance of artificial intelligence (AI) algorithms in practice depends on the realism and correctness of the data, models, and feedback (labels or rewards) provided to the algorithm. This paper discusses methods for improving the realism and ecological validity of AI used for autonomous cyber defense by exploring the potential to use Inverse Reinforcement Learning (IRL) to gain insight into attacker actions, utilities of those actions, and ultimately decision points which cyber deception could thwart. The Tularosa study, as one example, provides experimental data of real-world techniques and tools commonly used by attackers, from which core data vectors can be leveraged to inform an autonomous cyber defense system.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 54 references that are in the map. First posted on arXiv 31 Aug 2021.

Builds on

2 of 54 references.

References · 2

  1. 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 482
  2. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning
  2. 2024Exploring reinforcement learning for incident response in autonomous military vehicles
  3. 2026Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
  4. 2025An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents
  5. 2026Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers
  6. 2024Multi-Agent Actor-Critics in Autonomous Cyber Defense
  7. 2026Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
  8. 2025Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms