Finding the Optimal Security Policies for Autonomous Cyber Operations With Competitive Reinforcement Learning
Bibliographic record
Abstract
Reinforcement Learning (RL) has been responsible for some of the most impressive advances in the field of Artificial Intelligence (AI). Research in competitive RL has shown that multiple agents competing in an adversarial environment can learn simultaneously in order to discover their optimal decision-making policies. Competitive RL algorithms have been used to train performant AI for a variety of games and optimization problems. Cybersecurity is a domain where the emerging research in competitive RL is being considered for its real-world application. In order to develop Automated Cyber Operations (ACO) tools using RL, various open-source environments are available to simulate network security incidents. However, the existing research in these environments is typically one-sided: a Red or Blue agent is trained to optimize their decision-making against a static opponent. Competitive RL has not been attempted in these emerging environments. In this work, we trained agents using competitive RL to approximate their game theory optimal policies in a simulated ACO environment. We showed that near-optimal behavior was reached gradually through fictitious play demonstrating that these strategies can be used to approximate the optimal policies for agents involved in sophisticated sequential decision-making during a cyber attack.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".