A Blended Active Detection Strategy for False Data Injection Attacks in Cyber-Physical Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, different solutions have been proposed to detect advanced stealthy cyber-attacks against networked control systems. In this article, we propose a blended detection scheme that properly leverages and combines two existing detection ideas, namely, watermarking and moving target. In particular, a watermarked signal and a nonlinear static auxiliary function are combined to both limit the attacker's disclosure resources and obtain an unidentifiable moving target. The proposed scheme is capable of detecting a broad class of false data injection attacks, including zero-dynamics, replay, and covert attacks. Moreover, it is shown that the proposed approach mitigates the drawbacks of standard moving target and watermarking defense strategies. Finally, an extensive simulation study is reported to contrast the proposed detector with recent competitor schemes and provide tangible evidence of the effectiveness of the proposed solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it