Advancing the RSA Cryptanalysis: An Experimental Demonstration of Plaintext Recovery Using Neural Networks
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it