Current State of API Security and Machine Learning
Bibliographic record
Abstract
The adaptation of application program interface (API)s in every enterprise is the emerging business trend, and at the same time it diversifies the threat domain for businesses. APIs are becoming the new and most important infrastructure layer on the Internet and are the most vulnerable points of attack in modern systems. Each API adds new dimensions to security threats and attack vectors to corporate data and applications, therefore critically forfeiting the business systems. Traditional security features for API protection are provided through API gateways, and it had been nothing more than API keys and username/password combinations (HTTP authentication). On the other hand, intruders and hackers are getting smarter. Combining the proliferation of social engineering platforms with recent technological advancements, the ability to gain access to confidential data has become both easier and common [1], [2]. APIs funnel data among applications, a multitude of various API users, and cloud infrastructure, therefore sensitive or confidential information might get exposed to unauthorized users, if API security is not carefully crafted. Using a holistic approach to securing APIs not only addresses the vulnerability issues, but offers protection for all of the infrastructure, networks and information.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".