Autonomous network cyber offence strategy through deep reinforcement learning
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
Network defensive cyber operations (DCO) are inherently multi-domain, traversing different network segments and functional levels that encompass networking devices, protocols, services, applications and users. However, recent AI technologies threaten to complicate DCO as they can learn and adapt novel cyber-attack decision strategies to defeat countermeasures. Specifically, Reinforcement and Deep Reinforcement Learning (RL/DRL) are AI technologies for sequential decision-making in complex environments that have exceeded human master level performance in several domains through their ability to navigate the enormous state spaces of these environments. To investigate the effectiveness of AI-empowered autonomous cyber attacks, this work presents a preliminary study of DRL algorithms in training red AI agents in multi-domain computer networks. Employing a cyber network attack environment in the OpenAI Gym, the agents are trained to automatically establish and optimize their attack decision strategy. Different DRL algorithms are tested to evaluate the effectiveness against a selected set of network, service and application configurations, and to compare their stability, robustness and generalization characteristics. The results illustrate the potential of DRL-based cyber agents for researching new schemes to support cyber offence and defence operations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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