«Ақпараттық қауіпсіздік» арнайы курсының мазмұнын оқу процесінде жетілдіру: машиналық оқытудың көмегімен кибершабуылдарды анықтау

2024

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.26577/jes20247801015
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Currently, as a result of the development of information and communication technologies, facts are being revealed that the detection of information attacks using existing traditional methods is ineffective. Meanwhile, machine learning algorithms find effective solutions to problems such as classifying Internet Protocol traffic, filtering malformed traffic to detect attacks, and responding to trivial attacks. Therefore, our research work was based on improving the content of a special course on information security of the educational program «6B01511-Informatics» of the L. N.Gumilyov Eurasian National University in accordance with the development of modern information technologies. Accordingly, the goal of the research work is to identify cyber-attacks using neural networks. In accordance with the purpose of the research work, work was carried out such as collecting data with cyber-attacks, classifying data into classes, developing a neural network model, identifying cyber-attacks based on the developed model. The developed model can be used to define any process in any area. According to the survey results, we see that the level of student satisfaction with the content of the special course increased by 55%. Thus, improving the content of education in the field of information security has yielded positive results. In the future, the updated special course will be available to the country's leading higher education institutions. Keywords: neural networks, cyber-attack, machine learning, educational system, multilayer perceptron.