An Approach to Estimate the Impact to Mission Functions Following a Cyber Breach
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
The ability to plan, execute, and oversee military operations relies on well-defined operational functions, which for the Canadian Armed Forces (CAF) are command, sense, act, shield, and sustain. These functions, crucial in collaborative engagements and coalition campaigns, constitute a tailored balance essential for battlespace roles and are increasingly conducted in and through cyberspace. However, the increased frequency and sophistication of cyber attacks targeting the military’s operations in and through cyberspace pose a threat to these foundational pillars of military capability, potentially endangering ongoing missions. Understanding the consequences of a cyber breach on these mission functions is therefore imperative for commanders to make informed decisions. To address this need, we propose employing Cyber Damage Assessment (CDA) measures to estimate the impact on specific operational functions following a cyber breach. Our approach involves ingesting operational and business data to determine metrics and measures representing losses resulting from a cyber breach. We then use fuzzy logic to aggregate measures for multiple key performance indicators for cyber damage with commanders’ experiential knowledge regarding military capabilities and their corresponding losses, thereby providing estimates to the impacts on specific military functions following a cyber breach. Our results, which are self-consistent, offer impact estimates aligned with commanders’ experiential insights, thus providing valuable input for decision-making in the face of a cyber breach scenario.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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