Assessment of Interdependencies between Communication and Information Technology Infrastructure and other Critical Infrastructures from Public Failure Reports
Bibliographic record
Abstract
Failure in Communication and Information Technology Infrastructure (CITI) can disrupt the effective functionalities of many of the critical infrastructures. Conversely, failures in other infrastructures can also propagate to CITI and hence disrupt the operation of these interconnected systems. Understanding the origin of these failures, their propagation patterns and their impacts can give us important ideas about infrastructure interdependencies and can be used for secure and reliable infrastructure design and operation. In this research we have taken the approach to use public domain failure reports to identify these interdependencies. We have developed a methodology to collect and categorize these reports and defined a set of critical attributes to extract meaningful information from them. Using this approach we have analyzed 12 years of infrastructure failure reports from ACM's RISKS forum. Our results have shown interdependencies between CITI and other critical infrastructures in different dimensions, such as origin of failures, impacts of failures in spatial and temporal dimensions, how they have affected public safety; and how failures have propagated from one infrastructure to another. Results obtained from the analysis of real life failure cases, which happened over a considerable span of time, should be useful for infrastructure researchers and practitioners. This paper also discusses the difficulties while using public domain data in an academic research.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".