Week 8: Refining the Cryptanalysis

Welcome back to my blog, everyone. Traditional keys are like basic locks with numerous predictable patterns. They are quite easy to break through. In contrast, AI-generated keys are far more organic in nature. Now that a clean cryptanalysis pipeline has been built, by outputting 10,000 128-bit binary keys, running a full NIST STS test suite, along with the Chi-Square and Runs Tests, and entropy histograms, my data can now be used for testing cryptographic entropy in general.

I’ve made the GitHub repository more clean and streamlined. We can now see clear structure, many charts, and I have even added quite a few things to the README section. The final visuals have an embedded markdown. Alongside this work, I have deepened my understanding of the Assembly language (especially its x86 variant), CPU architecture, and how the Terminal allows for direct system-level access. Learning how these systems operate at a low level has made me deeply appreciate the mechanisms behind what truly makes data reliable and unreliable. Occasionally, when working with GitHub, a few problems appear with the Terminal, but they have been resolved.

I have also worked on my Literature Review, which takes my results from this project as well as insight from the articles I have chosen, to merge them together in that final paper on how AI-generated keys truly compare to traditional models, both theoretically and empirically. If any of you are interested in cybersecurity, cryptography, cryptanalysis, or even just applying randomness testing with at least some knowledge of Python, come and explore the repository.

GitHub Repository: https://github.com/JustnEye/AI-Random-KeyStudy

Thanks for reading. I will keep you updated on my Literature Review, and my continued developments with testing different key sizes and a multitude of other mechanisms.