Covert channels in stochastic 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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it