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Record W2377575537

Canada's cyber warfare capabilities

2013· article· en· W2377575537 on OpenAlexaboutno aff
Bryan Lee, Sam Liles

Bibliographic record

VenueAnnual Information Security Symposium · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCyberspaceCyberwarfareOffensiveComputer securityAdversaryContext (archaeology)Government (linguistics)Information warfareCyber-attackCyber threatsPolitical scienceInternet privacyEngineeringComputer scienceThe InternetOperations research
DOInot available

Abstract

fetched live from OpenAlex

This paper discusses Canada and its ability to wage cyber warfare. Several definitions of cyber warfare are presented and discussed, as well as the motives and potential actors behind a cyber attack. Several definitions of cyberspace are also discussed in order to provide a context for the domain of cyber warfare. A case is then made for why anyone should care about cyber warfare. Cyber attacks are a threat to a nation's security. Cyberspace must be considered a fourth domain of war, with the other three domains being land, air, and sea. There are many dangers within cyberspace that can affect individuals, corporations, and nation-states. Canada's cyber warfare capabilities are then examined. Both offensive and defensive capabilities are considered, with the focus of much of the research being on defensive capabilities. Canada recently released a cyber security strategy which is discussed in detail. Furthermore, capabilities of several government organizations are examined. Finally, a comparative assessment of Canada's capabilities within cyberspace is given. Canada's capabilities are found to be less than adequate to defend against a cyber engagement by an enemy nation-state. However, it is likely that many nation-states would be unable to defend against such an engagement from a knowledgeable and timely attacker.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.006
GPT teacher head0.233
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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