Advancing the RSA Cryptanalysis: An Experimental Demonstration of Plaintext Recovery Using Neural Networks
Bibliographic record
Abstract
Cryptography is essential to cybersecurity since it guarantees data's secrecy, integrity, and validity against threats.The fast expansion of artificial intelligence (AI) in many applications needs the equal importance of mitigating AI-induced cybersecurity vulnerabilities and protecting AI from cyberattacks.This study investigates a plaintext recovery assault on the most common public key algorithm of RSA cryptographic technique using a 66-bit public key and a compact neural network with 1,276 parameters.The testing reaches Bit Probability Accuracy, precision, F1 score, recall, and specificity of an average of 95% for each, a system of combined bits of accuracy of 85% tested with a full dataset, and illustrating how AI may enhance cryptanalysis under certain situations by identifying three out of four plain texts from the amalgam of ciphertext and the public key.These results underscore the possible hazards linked to extensive AI systems that may train models with millions to billions of parameters, emphasizing the need for more research into AI's function in cryptanalysis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".