ATMoS+: Generalizable Threat Mitigation in SDN Using Permutation Equivariant and Invariant Deep Reinforcement Learning
Bibliographic record
Abstract
Software-defined networking creates new opportunities for automated network security management by providing a global network view and a standard interface for configuring network policies. Previously, we proposed a general framework, called ATMoS, for autonomous threat mitigation using reinforcement learning (RL) in software-defined networks. Using a suitable set of host simulations and based on observations from an arbitrary network monitoring infrastructure, ATMoS can autonomously mitigate threats by moving hosts between a set of virtual networks that embody different network policies. In this article, we propose ATMoS+, which extends the RL agent in ATMoS with a novel Deep Q-Network architecture. The deep RL agent in ATMoS+ leverages permutation-invariant and permutation-equivariant set functions to relax previous assumptions on the number of network hosts and their ordering. We showcase that the proposed deep RL agent is scalable and generalizes to an arbitrary-sized network without additional retraining, scales with the number of hosts, and accommodates several different types of threat alerts.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".