Cyber Threats to Canadian Federal Election: Emerging Threats, Assessment, and Mitigation Strategies
Bibliographic record
Abstract
As Canada prepares for the 2025 federal election, ensuring the integrity and security of the electoral process against cyber threats is crucial. Recent foreign interference in elections globally highlight the increasing sophistication of adversaries in exploiting technical and human vulnerabilities. Such vulnerabilities also exist in Canada's electoral system that relies on a complex network of IT systems, vendors, and personnel. To mitigate these vulnerabilities, a threat assessment is crucial to identify emerging threats, develop incident response capabilities, and build public trust and resilience against cyber threats. Therefore, this paper presents a comprehensive national cyber threat assessment, following the NIST Special Publication 800-30 framework, focusing on identifying and mitigating cybersecurity risks to the upcoming 2025 Canadian federal election. The research identifies three major threats: misinformation, disinformation, and malinformation (MDM) campaigns; attacks on critical infrastructure and election support systems; and espionage by malicious actors. Through detailed analysis, the assessment offers insights into the capabilities, intent, and potential impact of these threats. The paper also discusses emerging technologies and their influence on election security and proposes a multi-faceted approach to risk mitigation ahead of the election.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".