Discovering Blue Team Solutions for an Autonomous Cyber Operations Challenge using an Evolutionary Heuristic Search
Why this work is in the frame
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Bibliographic record
Abstract
An approach to autonomous network defense is proposed that utilizes an evolutionary strategy to optimize default heuristic agents. The default heuristic agent is used to impart knowledge regarding network architecture, i.e. what the key infrastructure and bottlenecks might be. Evaluation takes place using the TTCP CAGE Challenge 2 framework where there are green (normal user), blue (defense), and red (attack) team agents. Unlike the deep learning solutions that have dominated this challenge, we are able to demonstrate that competitive solutions can be evolved that transfer knowledge from a blue team default heuristic. The resulting combined blue team is able to place second relative to the original 17 entries submitted to the TTCP CAGE Challenge 2 competition while maintaining knowledge of the solution.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it