Decoy-based Moving Target defense Against Cyber-physical Attacks On Smart Grid
Bibliographic record
Abstract
The design of successful covert cyber-physical attacks against smart grids requires a good level of knowledge about the dynamics of the target power system. Consequently, in the reconnaissance phase of a cyber-physical attack on a power system, the attacker usually needs to perform an accurate identification of the dynamics of the underlying control system. To degrade the accuracy of the system identification process, artificial noise can be added to the system measurements sent from the plant to the controller. While this approach might be effective in degrading the accuracy of recovering the parameters of the underlying target system, it comes at the expense of degrading the control system performance. In this paper, and inspired by the concept of decoy flare in air defense, a moving target defense mechanism is developed by leveraging an auxiliary set of virtual state-based decoy systems. More precisely, in this approach, the plant maintains and simulates a set of several decoy system models, designed to be indistinguishable from actual system models. At each time step, the plant sends a randomly permuted version of the corresponding measurements, of both the decoys and real system, to the controller which then evaluates and sends the corresponding optimal control of each system. The plant applies the received control inputs to the corresponding decoy models and the real system, respectively. The indistinguishability of the deployed decoy models, combined with the time-varying nature of the utilized permutation and system parameters, hinders the attacker' ability to perform an accurate system identification process. The effectiveness of the proposed approach is confirmed by considering an application example of an Automatic Generation Control (AGC) system. Based on our simulation results, the proposed decoy-based approach degrades the attacker' ability to correctly identify the underlying state-space model of the considered system from the intercepted control inputs and sensor measurements. It also does not impose any penalty on the control performance of the underlying system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".