Towards Autonomous Network Defense: Reinforcement Learning Environment for a Defense Agent
Bibliographic record
Abstract
In the present cyberspace, it is evident that the cyber attacks occur at a high pace that will surpass the human ability to respond in a timely fashion. Nations and government entities recognize the importance of addressing this issue and are increasingly emphasizing the necessity of autonomous defense systems. Defining autonomous defense entails deploying autonomous cyber agents and mechanisms to test these agents. Cyber agents are typically evaluated through cyber exercises, and the entity responsible for defensive tactics and maintaining internal network defense during the exercise is termed as a Blue Agent or a Defense Agent. The capacity of Reinforcement Learning (RL) to adapt and respond to novel threats makes it particularly valuable for enhancing cybersecurity measures. In this research, we leverage a well-known API, suited to create an RL-based environment for testing and training a defense agent. This environment is designed specifically to facilitate the RL agent in detecting multi-step aggressive access behavior. While a more sophisticated agent is not yet operational, the environment has been successfully tested against a rudimentary test agent, indicating its effectiveness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".