A Gain Randomization Framework Against Inference Attacks on Control Systems
Bibliographic record
Abstract
This paper develops a gain randomization framework against inference attacks on feedback control systems where an adversary with access to the states of the system attempts to infer the system model. To prevent the inference attack, the control gain of the system at each time step is randomly selected from a predefined set of control gains. We cast the gain selection problem as an optimal control problem where a gain selection policy at each time step selects a control gain according to a probability distribution such that (i) quadratic control cost is minimized and (ii) the uncertainty level of the adversary about selected control gain is maximized. In our formulation, the gain selection policy is allowed to depend on the entire history of the state measurements and the uncertainty level of the adversary about the control gain is captured by the Kullback-Leibler (KL) divergence between a uniform distribution and the posterior distribution of the feedback gains given the history of the system states. We first derive the backward Bellman optimality equation for the gain selection problem and study the structural properties of the optimal gain selection policy. Our results show that the optimal gain selection policy only depends on the current state of the system, rather than the entire history of the states, which renders the optimal gain selection problem to a non-linear Markov decision process. We next derive a policy gradient theorem for the gain selection problem which provides an expression for the gradient of the objective function of the gain selection problem with respect to the parameter of a stationary (time-invariant) policy. The policy gradient theorem allows us to develop a stochastic gradient descent algorithm for computing an optimal policy. We finally demonstrate the effectiveness of our results using a numerical example.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".