Detection of Replay Attack in Control Systems \nUsing Multi-Sine Watermarking
Bibliographic record
Abstract
Cyber-physical systems (CPSs) consist of networks of sensors, computers and actuators. This \nresearch studies a control system within a CPS in which the plant and controller are separated \ngeographically but connected through communication links. The links could be subject to security \nattacks. Recently, the research focus on attack detection has been growing rapidly. This thesis \naims to develop methods based on the dynamic models of CPS for detecting attacks. \nThis research focuses on detection of ”replay attacks”. First, it proposes a watermarking \nscheme based on injecting a sequence of multi-sine waves. The watermarking is designed in such \na way that the transient response to watermarking is suppressed. A design process is proposed to \nreach a compromise between (i) the ease of detection of watermarking effects in the output and (ii) \nthe limiting of output fluctuations due to watermarking (and loss of control quality). One of the \nbenefits of this method is that it only requires frequency response of the closed loop system at a set \nof frequencies; a model of system is not required. \nPower spectral density estimates based on periodograms of the plant output (received by the \ncontroller) are used to trace watermarking. Furthermore, replay attack detection by tracing watermarking \neffects in the residual of Kalman filters is also explored. \nA case study involving a laboratory water tank is used to explore the proposed method. The \nresults of linear and non-linear model simulations are presented and is shown that replay attacks \ncan be detected successfully.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".