Bibliographic record
Abstract
Varnost komunikacij in aplikacij je v sodobni digitalni družbi vse pomembnejša, tako na poslovnem kot zasebnem področju. Glede na trenutne nepredvidljive razmere, na območju Evropske unije in v svetu, pa se stopnja tveganja za kibernetski napad vsak dan povečuje. V prispevku bo predstavljena varnost elektronskih komunikacij in tistih aplikacij, ki jih poslovni svet najpogosteje uporablja. To so elektronska pošta, protokoli za oddaljen dostop pri delu od doma in ostali komunikacijski protokoli. Dotaknili pa se bomo tudi aplikacij z zasebnega področja, kot so Whatsup, Viber, Messenger, Zoom ter podobne. Vsem komunikacijskim kanalom je skupno, da uporabljajo šifrirane protokole, vendar tudi slednji niso vsi enako varni. Predstavili bomo, kako varno uporabljati aplikacije, kako varno komunicirati ter kakšne preventivne ukrepe mora poznati vsak uporabnik katerekoli elektronske naprave, da zmanjša možnost vdora ali okužbe z zlonamerno programsko opremo.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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; both teacher heads agree on what is shown here.
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".