Bibliographic record
Abstract
Abstract A covert channel is a communication channel that is not intended to exist, and that can be used to transfer information in a manner that violates the system security policy. Attackers can abuse such channels to exfiltrate sensitive information from cyber‐physical systems (CPSs), for example to leak the confidential or proprietary parameters in a control system. Furthermore, attacks against CPSs can exploit the leaked information about the implementation of the control system, for example to determine optimal false data injection attack values that degrade the system performance while remaining undetected. In this study, a control theoretic approach for establishing covert channels in stochastic CPSs is presented. In particular, a scenario is considered where an attacker is able to inject malware into the networked controller and arbitrarily alter the control logic. By exploiting such capability, an attacker can establish an illegitimate communication channel, for example to transmit sensitive plant parameters, between the networked controller and an eavesdropper intercepting the sensor measurements. The authors show that such a channel can be established by exploiting the closed‐loop system operations, a decoding mechanism based on an unknown input observer, and an error‐correcting coding scheme that exploits the control loop to obtain an implicit acknowledgement. A simple proof of concept implementation of the covert channel is presented, and its performance is evaluated by resorting to a numerical example. Finally, some defences and countermeasures are proposed against the proposed covert channel.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".