A Generic Blue Agent Training Framework for Autonomous Cyber Operations
Bibliographic record
Abstract
Sophisticated mechanisms for attacking a computer network are emerging, therefore it is of great importance that equally sophisticated mechanisms should be in place to defend against malicious attacks on the network. Autonomous cyber operations (ACO) is considered to be a potential option to provide timely defense against malicious attacks. In ACO, an agent that tries to attack a network is referred to as red agent, and an agent that defends against the red agent is called blue agent. In real scenarios, different kinds of red agents can attack a network, hence a blue agent needs to defend against a variety of red agents, each with their own attack strategy and specific goal. However, it is a challenging task to train a blue agent that is agnostic of the red agent. Hence, we present here a framework for generic blue agent training, i.e., training a blue agent that can defend against different kinds of red agents. The framework is a combination of reinforcement learning and supervised learning. Our results demonstrate that the presented framework for generic blue agent training does exhibit generic characteristics, and the framework does demonstrate better performance compared to an alternate approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".