A Novel Approach of 1-D Cellular Automata in Cryptosystem
Bibliographic record
Abstract
Cryptosystems worldwide employ techniques for the encryption and decryption of sensitive data, relying extensively on secret keys.In this context, the generation of a randomized, secured secret key and its size hold paramount significance in ensuring confidentiality, data integrity, and resistance to a plethora of security attacks, rendering it arduous for potential intruders to predict key sequences.This study aims to generate the highly secured randomized secret key, minimize the time complexity and ensure efficiency of the cryptosystem.In the key generation methodology presented, a novel approach was introduced, taking into account the receiver's credentials and employing the elementary cellular automata (CA).Rule 150 of CA was strategically leveraged to generate a secret key, undergoing numerous iterations to bolster security, intricacy, and to compound the predictability challenge of the key.Python was the chosen medium for the implementation of the proposed model.Time complexity was rigorously evaluated, and a comparative analysis was conducted against established cryptographic algorithms, notably Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES), to ascertain efficiency.Subsequently, a frequency analysis, underpinned by a letter frequency distribution chart, was undertaken to confirm the randomness of the resultant ciphertext (CT).To further validate the robustness of the proposed model, security assessments encompassing brute force attacks, CT attacks, known plaintext (PT) attacks, and chosen PT attacks were meticulously examined.Cumulative findings corroborate the heightened security and efficiency of the proposed model in contrast to its predecessors.Anticipated future research horizons include the potential incorporation of CA in domains such as image and video encryption, blockchain technology, cryptocurrency, and digital signatures, aiming to cultivate superior security infrastructures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".