The wireless control network: Monitoring for malicious behavior
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
We consider the problem of stabilizing a plant with a network of resource constrained wireless nodes. In a companion paper, we developed a protocol where each node repeatedly transmits an appropriate (stabilizing) linear combination of the values in its neighborhood. In this paper, we design an Intrusion Detection System (IDS) for this control scheme, which observes the transmissions of certain nodes and uses that information to (a) recover the plant outputs (for data-logging and diagnostic purposes) and (b) identify malicious behavior by any of the wireless nodes in the network. We show that if the connectivity of the network is sufficiently high, the IDS only needs to observe a subset of the nodes in the network in order to achieve this objective. Our approach provides a characterization of the set of nodes that should be observed, a systematic procedure for the IDS to use to identify the malicious nodes and recover the outputs of the plant, and an upper bound on the delay required to obtain the necessary information.
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) | 0.000 | 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