Facing cyberthreats in a crisis and post-crisis era: Rethinking security services response strategy
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 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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it