Identification of sources of failures and their propagation in critical infrastructures from 12 years of public failure reports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding the origin of infrastructure failures and their propagation patterns in critical infrastructures can provide important information for secure and reliable infrastructure design. Among the critical infrastructures, the Communication and Information Technology Infrastructure (CITI) is crucial, as it provides the basic mechanism for sharing information among all infrastructures. Failures in CITI can disrupt the effective functionality of the other critical infrastructures. Conversely, failures in the other infrastructures can also propagate to CITI, and hence disrupt the operation of all systems. In this study, we used public domain failure reports to identify the origin of these failures and their propagation patterns. We analysed 347 infrastructure failure cases reported from 1994 to 2005 in the Association for Computing Machinery's (ACM) RISKS forum. We studied these reports to determine the causes of infrastructure failures and their impact on CITI and other critical infrastructures in a number of dimensions, such as the origin of failures, impacts of failures in spatial and temporal dimensions, their effect on public safety and how failures propagate from one infrastructure to another. The results obtained from the analysis of these real-life failure cases, which occurred over a considerable timespan, should be useful to researchers and practitioners. This paper also discusses the difficulties and limitations of using public domain data in academic research.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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