Bibliographic record
Abstract
Introduction. With the rapid adoption of computer and networking technologies, educational institutions pay insufficient attention to the implementation of security measures to ensure the confidentiality, integrity and accessibility of data, and thus fall prey to cyber-attacks. Methods. The following methods were used in the process of writing the article: methods of generalization, analogy and logical analysis to determine and structure the motives for phishing attacks, ways to detect and prevent them; statistical analysis of data – to build a chronological sample of the world's largest cyber incidents and determine the economic losses suffered by educational institutions; graphical method – for visual presentation of results; abstraction and generalization – to make recommendations that would help reduce the number of cyber scams. Results. The article shows what role cyber security plays in counteracting phishing scams in the educational field. The motives for the implementation of phishing attacks, as well as methods for detecting and preventing them, have been identified and regulated. The following notions as "phishing", "submarine" and "whaling" are evaluated as the most dangerous types of fraud, targeting both small and large players in the information chain of any educational institution. An analytical review of the educational services market was conducted and a chronological sampling of the largest cyber incidents that occurred in the period 2010-2019 was made. The economic losses incurred by colleges, research institutions and leading universities in the world were described. It has been proven that the US and UK educational institutions have been the most attacked by attackers, somewhat inferior to Canada and countries in the Asia-Pacific region. It is found that education has become the top industry in terms of the number of Trojans detected on devices belonging to educational institutions and the second most listed among the most affected by the ransomware. A number of measures have been proposed to help reduce the number of cyber incidents. Discussion. The obtained results should be taken into account when formulating a strategy for the development of educational institutions, as well as raising the level of awareness of the representatives of the academic community in cybersecurity. Keywords: phishing, cyber security, cyber stalkers, insider threat, rootkit, backdoor.
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 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".