Bibliographic record
Abstract
In the current digital landscape, the demand for robust and layered security frameworks has intensified due to the increasing frequency and complexity of cyber threats. Cryptography and cybersecurity, though different in focus, are closely aligned and collectively form the core of modern digital defense strategies. Cryptography provides essential tools—such as encryption, hashing, and digital signatures—that safeguard the confidentiality, integrity, and authenticity of information. Cybersecurity builds on these techniques to implement policies and systems that protect against unauthorized access, data breaches, and malicious attacks. This paper examines the evolving connection between cryptography and cybersecurity, focusing on the development of cryptographic methods and their application in securing digital protocols like SSL/TLS, blockchain technologies, and public key infrastructures. Real-world use cases from healthcare, finance, and government are explored, highlighting the role of cryptographic integration in meeting regulatory standards like GDPR, HIPAA, and FISMA. The study also explores current challenges such as key management, scalability, and the threat posed by quantum computing. It further reviews emerging technologies including post-quantum cryptography, zero-knowledge proofs, and the integration of AI and machine learning for proactive, intelligent cybersecurity solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".