Security Evaluation of Industrial Organisations in an Isolated Region
Bibliographic record
Abstract
This paper presents the results of a cybersecurity audit conducted on thirty industrial SMEs located in a remote region of Eastern Canada. These firms face growing cyber threats while having limited access to security expertise and infrastructure. Using a mixed-method approach combining on-site technical assessments, structured interviews, and questionnaires, the study analyzes vulnerabilities through the TOE framework (Technological, Organizational, Environmental). Results show that 90% of companies lacked internal network segmentation, 80% were vulnerable to phishing attacks, and over 70% had no cybersecurity training or formal security policy. Based on these findings, we propose a set of low-cost and practical recommendations tailored to SMEs in isolated regions. These include awareness training, simple network protections, and internal policy development. The study highlights the urgent need for targeted cybersecurity strategies adapted to geographic and resource constraints, and contributes to both academic and operational understanding of how to improve cyber resilience in decentralized industrial ecosystems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".