Targeting Reputation: A New Vector for Attacks to Critical Infrastructures
Bibliographic record
Abstract
A substantial portion of critical information infrastructures in advanced economies comprises former public utilities, which in the 1980s/90s were fully or partially privatized, a change justified mainly on economic efficiency grounds. This entailed that these utility companies had to compete in the free market, thus being exposed to the same risks/opportunities as private companies. Much like businesses in other industrial sectors, utility companies have increasingly joined social media over the last decade, as ‘digital’ visibility through social networking platforms, such as Facebook, Twitter, and Instagram has become fundamental. The new (privatized) utilities have relied on marketing and ad campaigns to promote their business and generate revenues. Trust and reputation for companies are primary resources to attract new customers and/or keep old ones, especially for companies with a wide customer base. Trust and reputation are difficult assets to preserve on social media, as they can be subject to negative attacks, including fake campaigns. This paper is a probe that explores a potential attack vector to critical infrastructures via weakening customer and investor trust in (the now private) utilities by blemishing CII-utilities’ reputation on social media. More specifically, the paper considers the possibility of attacks that have the potential to undermine the stability and reliability of critical infrastructures and advances a preliminary justification of why that may happen. We do this by looking at cases in which negative social media campaigns with fake content have been successfully implemented via digital tools.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".