Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».