Enhanced Security in Information Transmission: Redundant Stream Ciphers with Time Delay Integration
Bibliographic record
Abstract
The paper addresses the challenge of enhancing the resilience of stream ciphers against attacks.It reviews existing approaches to stream cipher creation and proposes new methods that incorporate time delays to introduce gaps in the original message and embed additional bits.These methods result in a ciphertext that is longer than the original message, potentially altering the frequency if the overall transmission time is equalized.The paper explores methods that generate ciphers with varying lengths of bit insertion, enabling the creation of different length ciphers from a single input message.A method featuring frequent insertion of single bits, generated by additional pseudo-random number generators (PRNG), is implemented.The study examines both variable-length ciphergrams and fixed maximum insertion bit methods.A pseudo-random control bit sequence is employed to determine random insertion points or groups of additional bits, which are also generated pseudorandomly.To facilitate controlled delays, specialized hardware has been developed for both the transmitting and receiving ends, ensuring synchronous message transmission.The additional stability of these stream ciphers, enhanced through time delays, is further reinforced by bitwise mixing using the initial key gamma.These methods not only increase resistance to decryption but also introduce new challenges for cryptanalysts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".