Facing cyberthreats in a crisis and post-crisis era: Rethinking security services response strategy
Bibliographic record
Abstract
The recent years have witnessed two major events that have deeply impacted cybersecurity threats. First, the COVID-19 pandemic has drastically increased our dependence upon technology. From individuals to corporations and governments, the overwhelming majority of our activities moved online. As the proportion of human activities performed online is reaching new peaks, cybersecurity becomes a problem of national security. Second, the Russia-Ukraine war is giving us a glimpse of what cyberthreats may look like in future cyberconflicts. From data integrity to identity thievery, and from industrial espionage to hostile manoeuvres from foreign powers, cyberthreats have never been that numerous and diverse. Due to the increase of the magnitude, of the diversity, and of the complexity of cyberthreats, the current security strategies used to face cybercriminality won't be sufficient in the post-crisis era. Therefore, governments need to rethink globally their national security services response strategy. This paper analyses how this new context has impacted cybersecurity for individuals, corporations, and governments, and emphasis the need to reposition the economical identity of the individuals at the center of security response. We propose strategies to optimize law enforcement response from police to counterintelligence, notably through formation, prevention, and interaction with cybercriminality. We then discuss the possibilities to optimize the articulation of the different levels of security response and expertise, by emphasizing the need for coordination between security services, and by proposing strategies to include non-institutional players.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".