Minimizing attack radius of scheduled connections in WDM networks
Bibliographic record
Abstract
Transparent optical networks (TONs) has the advantage of handling high data rates through effective utilization of the fiber bandwidth. However, features like transparency in TONs makes such networks susceptible to several physical layer attacks. Hence, designing TONs with a capability of handling such high power jamming attacks is an important network security problem. In this work, we propose an integer linear program (ILP) formulation to control the propagation of these physical layer attacks in TONs, for the demands which need periodic bandwidth usage at certain predefined timings. There are two different approaches for handling these scheduled traffic demands, fixed window and sliding window. Our research deals with the sliding window scheduled traffic model, which is more flexible when compared with fixed window, as the start and end timings of the demand are unknown and they slide within a larger window setting. Hence, we present an ILP to handle the routing and wavelength assignment (RWA) problem for sliding window scheduled traffic model, with an objective to minimize the attack radius for all the commodities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".