Repairing Faulty Nodes and Locating a Dynamically Spawned Black Hole Search Using Tokens
Bibliographic record
Abstract
In a distributed cloud, it is crucial to detect and eliminate faulty network entities in order to protect network assets and mitigate the risks associated with constantly arising attacks. Much research has been conducted on locating a single static black hole, which is defined as a network site whose existence is known a priori and that disposes of any incoming data without leaving any trace of this occurrence. In this paper, we introduce a specific attack model that involves multiple faulty nodes that can be repaired by mobile software agents, as well as what we call a gray virus that can infect a previously repaired faulty node and turn it into a black hole. The Faulty Node Repair and Dynamically Spawned Black Hole Search (FNR-DSBHS) problem that proceeds from this model is much more complex and realistic than the traditional Black Hole Search problem. We first explain why existing algorithms addressing the latter do not work under this new attack model. We then distinguish between a one-stop gray virus that, after infecting a faulty node that has been repaired, can no longer travel to other nodes; and a multi-stop gray virus. We observe that, in an asynchronous network, a solution to the FNR-DSBHS problem is possible only when dealing with a single one-stop gray virus. In that specific context, we present a solution for an asynchronous ring network using a token model, that is, a ring in which a constant number of tokens is the only means of communication between the team of agents. We claim that, in such a ring, b + 9 agents can repair all faulty nodes as well as locate the black hole that is infected by this single one-stop gray virus. We prove the correctness of the proposed solution and analyze its complexity in terms of number of mobile agents used and total number of moves performed by these agents. We show that in the worst case, within O(kn2) moves, b + 9 agents suffice to repair b faulty nodes and report the location of the black hole that is infected, at any arbitrary point in time, by the one-stop gray virus.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".