Using Hybrid Neural Networks to Detect DDOS Attacks

Belej, Olexander; Halkiv, Lіubov

doi:10.1109/dsmp47368.2020.9204166

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Важно отметить, что нейронная сеть должна быть обучена на репрезентативных данных и регулярно обновляться, чтобы адаптироваться к изменяющимся угрозам. Она может использоваться в сочетании с другими методами обнаружения и защиты, образуя комплексную систему безопасности веб-приложений [8,9].…”

Section: удк 00405657 безопасный искусственный интеллектunclassified

Detection of Attacks on Web Application Using Self-Organizing Kohonen Maps

2024

Cybersecurity Issues

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Purpose of the article: improving the effectiveness of web application attack detection. Method: using self-organizing maps for real-time detection of attacks on web applications. The result: The self-organizing map was considered as a means of detecting anomalous data. The possibility of integrating the self-organizing map with a web application firewall system for protection against attacks and vulnerabilities was analyzed. A software tool was implemented to detect anomalies in HTTP requests and responses using the self-organizing map. Parameters for the neural network were selected, and metrics were chosen for analysis by the neural network from the HTTP requests. The implemented software tool was integrated with the NGINX web server using the NGX JavaScript module. Functional and load testing of the integrated system was conducted using the OWASP ZAP security scanner. The results obtained led to the conclusion that the self-organizing map effectively detects anomalies, but it should be used in conjunction with template-based analysis methods.Practical value: within the framework of the study, a methodology for testing web application security tools has been developed. The software that makes up the test bench is described. The metrics that allow you to objectively assess the effectiveness of the web application protection tool are listed.

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Section: удк 00405657 безопасный искусственный интеллектunclassified

Detection of Attacks on Web Application Using Self-Organizing Kohonen Maps

2024

Cybersecurity Issues

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“…Но обычно, данные алгоритмы используются как часть более сложной системы. К примеру алгоритм K-Means в работах [10][11] используется совместно с генетическим алгоритмом, а сеть Кохонена в [12][13][14] с персептроном.…”

Section: гибридные системыunclassified

Comparison of G-Means Algorithms and Kohonen Network in Solving Clustering Problems

Sudakov

Дмитриев

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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Purpose: In this paper, the question of how to improve a self-organizing neural network consisting of a bundle of clustering algorithm and a multilayer perceptron for data verification tasks in the absence of training pairs is considered. Design/methodology/approach: The most popular clustering algorithm is the Kohonen network, but today it is not the only algorithm capable of performing the task quickly and accurately. The paper compares the Kohonen network and the G-Means algorithm. The principle of operation of these two algorithms is briefly analyzed. The accuracy of these algorithms and the speed of their learning are compared. Findings: By conducting experiments, conclusions were drawn about the speed and accuracy of the algorithms. Originality/value: The relevance of this work lies in the fact that the preparation of training pairs for intelligent systems and the process of learning with a teacher is a resource-intensive task. The systems of self-learning algorithms under consideration will significantly increase the learning rate, as well as eliminate the need for manual classification of data and the creation of training pairs for the perceptron, which in turn will allow you to create a self-learning system with the ability to generalize and predict.

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