Cryptanalysis of Practical Optical Layer Security Based on Phase Masking of Mode-Locked Lasers and Multi-Homodyne Coherent Detection
Bibliographic record
Abstract
We have previously suggested a promising approach for optical layer security, incorporating an all-optical spectrum spreading, spectral phase-encoding time-spreading, and noise-protected coherent communication system. An authorized receiver with the spectral phases key can evoke a multi-homodyne coherent detection (MHCD) to reconstruct the noise-submerged signal. Unless deciphered in real-time, by all-optical means and with the correct phases key mask, an adversary cannot reconstruct the transmitted data, which is permanently lost. This feature prohibits unauthorized offline processing, regardless of the resources and efforts available to the adversary, thus making data-in-transit record-proof and resilient to any computational power, including the quantum computer. In this work, we present a novel security analysis for this approach, where three different types of attacks are proposed and thoroughly studied: Naive, Analytic, and Greedy. These algorithms represent different approaches for all-optical phases key cracking. We formulated a mathematical model for a Naive attacker who trials an arbitrary phase mask. In the Analytic approach, the attacker studies the encoding system by trialing arbitrary test patterns. In contrast, the attacker who employs the Greedy approach maximizes his performance in each step until the desired signal-to-noise ratio (SNR) level is obtained. We analyze these approaches analytically and discuss their cryptanalysis aspects concerning performance, complexity, and the photonic hardware used to decode the phase mask. Our simulations and models suggest a set of conditions for an all-optical transmission system that is impervious to cryptography attacks.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".