Accelerating Autonomous Cyber Operations: A Symbolic Logic Planner Guided Reinforcement Learning Approach
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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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".