Bibliographic record
Abstract
Suppose information security starts to embrace the reality that failure will happen. In that case, we can move from trying to build the perfectly secure system to continue asking questions like “how much vulnerability do I have and what control do I need to be effective?” This paper proposes Security Chaos Engineering as a method to expose API vulnerabilities and enhance API security. RESTful API has gained popularity in recent years due to its reusability, flexibility and natural adaptation to modern web application, mobile application, and cloud computing. However, ensuring secure API/data access and hence mitigating reputational and/or financial damage to the organization is still in its early stage. Foundational security protection mechanisms include transport layer security, authentication / authorization of the consumer (either individual or application). To complete the spectrum of secure API access and provide advanced protection, there is much more to consider: mitigation of API specific vulnerabilities at design and implementation time. API Security using Chaos Engineering is an approach for learning about system security behavers when using APIs by applying empirical exploration. Security Chaos Engineering is the discipline of experimenting to build confidence in the system’s security and see how a system can withstand threats in production. Security Chaos Engineering isn’t about creating chaos. It is about making the security chaos inherent in the system visible.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".