Accelerating Autonomous Cyber Operations: A Symbolic Logic Planner Guided Reinforcement Learning Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Training a reinforcement learning agent to learn network penetration testing is challenging due to the partially-observable, non-deterministic environment. The large action space leads to extended training time, an issue of particular concern in mission-oriented network deployment that requires timely hardening tests. Current solutions for automating penetration testing are divided between reinforcement learning (RL) and AI planning. This work integrates the two paradigms and establishes a neuro-symbolic agent training system through an interactive symbolic logic engine. Two methods are examined for accelerating the pentest agent training in this system, namely: invalid action masking for Deep Q-Networks and using a symbolic logic engine as an environment driver. The results show that invalid action masking is highly effective at reducing the number of steps to convergence, while the logic-based simulator provides a significant per-step performance improvement to speed up training. These results highlight that a hybrid neuro-symbolic approach is a viable, and perhaps even necessary, method for developing and improving cyber RL agents.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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