Securing Power System Data in Motion by Timestamped Digital Text Watermarking
Bibliographic record
Abstract
Modern power systems rely on Wide Area Network (WAN) for real-time data transmission and control. However, integrating WAN introduces vulnerabilities that adversaries can exploit, leading to inaccurate information, compromised decisions, and potential grid instability. The Automatic Generation Control (AGC) system, responsible for maintaining power balance, is an attractive target for attackers due to its reliance on WANs for critical signal transmission. Traditional security mechanisms like encryption, multi-factor authentication, antivirus, etc., may not be suitable for power systems due to system priorities, processing limitations, and compatibility issues. To address this, we propose a novel digital text watermarking-based approach that ensures data integrity and detects replay attacks. The watermarking technique dynamically embeds a unique timestamped watermark into sensor data, enabling detection of unauthorised modifications. The algorithm confirms data integrity by comparing the extracted and expected watermark utilizing Damerau-Levenshtein index. Timestamp analysis is employed to assist in identifying replay attacks. Notably, our technique is independent of system scale, making it practical for real-world implementation. Furthermore, the framework can be extended to secure other power system applications, enhancing overall cyber resilience. Experimental results demonstrate the efficacy of our algorithm in AGC systems while ensuring almost zero performance loss to system operations.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".